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Among several devastating natural hazards, flooding is a common and serious threat to society causing huge loss of lives, properties, and infrastructure throughout the world. The intensity and frequency of this extreme weather event are expected to increase due to significant changes in the present‐day climate and land use and land cover (LULC) pattern. India has a very systematic and organized structural program and policies but lacks proper implementations, and adverse effect of climate change and the extreme event goes on in society. This paper is an analysis of floods in India and hazards due to climate change and LULC change patterns. Three models, namely "Eco‐biogeography‐based optimization (EBO), Random forest (RF), and Support vector machine (SVM)" were used to obtain the final output to prepare a "Flood susceptibility map". The result was validated through the "Receiver operating characteristics (ROC)" with "Area under curve (AUC)" values. The future rainfall scenario has been estimated by considering the "General circulation models" through different "shared socioeconomic pathways (SSPs)". The values of AUC are 0.915 (EBO), 0.887 (RF), and 0.869 (SVM), respectively. After consideration of different SSPs, the result shows that there is an increasing tendency of flood hazards in the projected period. Among all the employed modelling approaches, the EBO model has notable potential in delineating the possible flood‐prone regions for effective flood planning and management. Decision‐makers can benefit from country‐specific information and regional planner to implement sustainable and long‐term measures to overcome this type of hazardous situation. [ABSTRACT FROM AUTHOR]

This paper assesses the long-term combined effects of land use (LU) and climate change on river hydrology and water scarcity of two rivers of the Western Ghats of India. The historical LU changes were studied for four decades (1988–2016) using the maximum likelihood algorithm and the long-term LU (2016–2075) was estimated using the Dyna-CLUE prediction model. Five General Circulation Models (GCMs) were utilized to assess the effects of climate change (CC) and the Soil and Water Assessment Tool (SWAT) model was used for hydrological modeling of the two river catchments. To characterize granular effects of LU and CC on regional hydrology, a scenario approach was adopted and three scenarios depicting near-future (2006–2040), mid-future (2041–2070), and far-future (2071–2100) based on climate were established. The present rate of LU change indicated a reduction in forest cover by 20% and an increase in urbanized areas by 9.5% between 1988 and 2016. It was estimated that forest cover in the catchments may be expected to halve compared to the present-day LU (55% in 2016 to 23% in 2075), along with large-scale conversion to agricultural lands (13.5% in 2016 to 49.5% in 2075). As a result of changes to LU and forecasted climate, it was found that rivers in the Western Ghats of India might face scarcity of fresh water in the next two decades until the year 2040. However, because of large-scale LU conversion toward the year 2050, streamflow in rivers might increase as high as 70.94% at certain times of the year. Although an increase in streamflow is perceived favorable, the streamflow changes during summer and winter may be expected to affect the cropping calendar and crop yield. The changes to streamflow were also linked to a 4.2% increase in ecologically sensitive wetlands of the Aghanashini river catchment. [ABSTRACT FROM AUTHOR]

Assessment of climate and land use changes impact including extreme events on the sediment yield is vital for water and power stressed countries. Mangla Reservoir is the second-largest reservoir in Pakistan, and its capacity is being reduced due to rapid sedimentation and will be threatened under climate and land use changes. This paper discusses the consequences of climate and land use change on sediment yield at Mangla Dam using General Circulation Models (GCMs), Land Change Modeler (LCM), Soil and Water Assessment Tool (SWAT) model after calibration and validation. Results show that over the historical period temperature is observed to increase by 0.10oC/decade and forest cover is observed to reduce to the level of only 16% in 2007. Nevertheless, owing to the forest conservation policy, the forest cover raised back to 27% in 2012. Anticipated land use maps by using LCM of 2025, 2050 and 2100 showed that the forest cover will be 33%, 39.2%, and, 53.7%, respectively. All seven GCMs projected the increase in temperature and five GCMs projected an increase in precipitation, however, two GCMs projected a decrease in precipitation. Owing to climate change, land use change and combined impact of climate and land use change on annual sediment yield (2011-2100) may vary from -42.9% to 39.4%, 0% to -27.3% and, -73% to 39.4%, respectively. Under climate change scenarios projected sediment yield is mainly linked with extreme events and is expected to increase with the increase in extreme events. Under land use change scenarios projected sediment yield is mainly linked with the forest cover and is expected to decrease with the increase in forest cover. The results of this study are beneficial for planners, watershed managers and policymakers to mitigate the impacts of climate and land use changes to enhance reservoir life by reducing the sediment yield. [ABSTRACT FROM AUTHOR]

Groundwater plays an important part in protecting the ecological values of the earth's systems. Assessment of groundwater (GW) recharge due to change in land use/land cover (LULC) and climate variables is essential for integrated water management and adaptation. This study is an attempt to check the combined influence of change in LULC and climate variables on GW recharge in Kallada river basin, Kerala, India. The LULC map is predicted for the year 2030 based on LULC maps during the years 1985, 1995, 2005 and 2015 using Markov chain and multilayer perceptron model. The major LULC category in the study area is plantation with an area of 797.8 km2 (49.6% in 2015) and dense forest with an area of 366.5 km2 (22.8% in 2015). Considering the period 1985–2005, a decrease of 7.43% dense forest and an increase of 2.55% built area were observed. The predicted LULC shows that there is a reduction in plantation (3.77%) and dense forest (1.28%) and increase in built-up land (4.12%) by 2030 as compared to the year 2015. The CMIP5 General Circulation Model is used for future projections of climate variables, namely daily precipitation, maximum temperature and minimum temperature under RCP 4.5 and RCP 8.5 scenarios. Multilayer perceptron neural network model was used for statistical downscaling of GCM projections. The SWAT model was used to estimate the groundwater recharge for the year 2030 using the predicted LULC map and hydro-meteorological variables. The results illustrate a significant drop in GW recharge in Kallada river basin by the year 2030. Average GW recharge in the Vettikavala and kottarakkara, which is in the western region of the basin, is reduced to 4.6% and 9.3% under RCP 4.5 and RCP 8.5 scenarios, compared to the average GW recharge in 2005, which was 24.8% of rainfall. The decline in the groundwater recharge in the western regions may be due the increase in built-up area and decrease in plantation. The proposed model results provide a reliable insight for water resources planners in conducting future research on groundwater resources. [ABSTRACT FROM AUTHOR]

The aim of this paper is to identify relative roles of different land-atmospheric conditions, apart from sea surface temperature (SST), in determining early vs. late summer monsoon intensity over India in a high resolution general circulation model (GCM). We find that in its early phase (June-July; JJ), pre-monsoon land-atmospheric processes play major role to modulate the precipitation over Indian region. These effects of pre-monsoon conditions decrease substantially during its later phase (August-September; AS) for which the interannual variation is mainly governed by the low frequency northward propagating intraseasonal oscillations. This intraseasonal variability which is related to mean vertical wind shear has a significant role during the early phase of monsoon as well. Further, using multiple linear regression, we show that interannual variation of early and late monsoon rainfall over India is best explained when all these land-atmospheric parameters are taken together. Our study delineates the relative role of different processes affecting early versus later summer monsoon rainfall over India that can be used for determining its subseasonal predictability. [ABSTRACT FROM AUTHOR]

General circulation models (GCMs) are routinely used to simulate future climatic conditions. However, rainfall outputs from GCMs are highly uncertain in preserving temporal correlations, frequencies, and intensity distributions, which limits their direct application for downscaling and hydrological modeling studies. To address these limitations, raw outputs of GCMs or regional climate models are often bias corrected using past observations. In this paper, a methodology is presented for using a nested bias-correction approach to predict the frequencies and occurrences of severe droughts and wet conditions across India for a 48-year period (2050-2099) centered at 2075. Specifically, monthly time series of rainfall from 17 GCMs are used to draw conclusions for extreme events. An increasing trend in the frequencies of droughts and wet events is observed. The northern part of India and coastal regions show maximum increase in the frequency of wet events. Drought events are expected to increase in the west central, peninsular, and central northeast regions of India. [ABSTRACT FROM AUTHOR]

Mankind is currently facing a crucial challenge in terms of climate change which may result in severe drought in many areas across the world. The present study analysed the joint return periods of meteorological drought events for historical and future projections across the Vidarbha region of India. To describe the drought occurrences in the area, the study used the standardized precipitation evapotranspiration index. The properties of each drought event, viz., duration, severity, and peak, were identified to model the multivariate drought risks. Several copula families were evaluated using statistical tests for joint dependency modelling of drought properties. The obtained joint distribution and selected univariate marginals are further used for estimating trivariate return periods for the historical and future climate change scenarios. The drought properties exhibited good interdependency justifying the use of a trivariate framework. The observed historical data of rainfall, maximum and minimum temperature were collected from the India Meteorological Department. The latest future projections of climate variables from coupled model intercomparison project phase 6 (CMIP6) datasets were acquired from NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) for six general circulation models. The copula-based trivariate frequency analysis was performed to compare the return periods associated with 'moderate' and 'above moderate drought' (i.e., combination of severe and extreme drought) events for the reference period (1981–2020) and future periods (2021–2100) under two SSP scenarios, SSP2-4.5 and SSP5-8.5. The 'AND' condition return period showed high drought risk during historical period covering a larger area. The findings of the study suggested that for 'AND' condition the trivariate frequency of above moderate droughts would increase in the future across Vidarbha region. The analysis showed that during near future more area will be under the risk of above moderate drought conditions compared to the far future, and the high-risk zones will be more under SSP5-8.5 than SSP2-4.5 scenario. The frequency of moderate drought episodes will be higher than the above moderate drought events, but the percentage area in the high-risk zone will be lower. The 'OR' condition return period projected alleviation of drought risk in the future, while the far future projected more area under drought risk compared to near future for the SSP scenarios. [ABSTRACT FROM AUTHOR]

Previous studies suggest that the winter surface freshening in the southeastern Arabian Sea (SEAS) contributes to the development of very high Sea Surface Temperatures (SST) thereby influencing the following summer monsoon onset. Here, we use forced and coupled simulations with a regional ocean general circulation model to explore the SEAS Sea Surface Salinity (SSS) variability mechanisms and impact on the monsoon. Both configurations capture the main SEAS oceanographic features, and confirm that the winter SSS decrease results from horizontal advection of Bay of Bengal freshwater by the cyclonic circulation around India during fall. A coupled model sensitivity experiment where salinity has no effect on mixing indicates that the salinity stratification reduces the SEAS mixed layer cooling by vertical processes by 3 °C seasonally. Salinity however enhances mixed layer cooling by a similar amount through concentrating negative winter surface heat fluxes into a thinner mixed layer, resulting in no climatological impact on SST and summer monsoon rainfall. The Indian Ocean Dipole (IOD) is the main driver of the winter SEAS SSS interannual variability (r ~ 0.8). Salty anomalies generated in the western Bay of Bengal during fall by positive IOD events are indeed transported by the cyclonic climatological coastal circulation, reaching the SEAS in winter. By this time, warm IOD-induced SST anomalies in the SEAS are already decaying, and the SEAS SSS anomalies hence do not contribute to their development. Overall, our model results suggest a weak climatological and interannual impact of the SEAS winter freshening on local SST and following monsoon onset. [ABSTRACT FROM AUTHOR]

The southwest monsoon delivers over 70% of India's annual rainfall and is crucial to the success of agriculture across much of South Asia. Monsoon precipitation is known to be sensitive to sea surface temperature (SST) in the Bay of Bengal (BoB). Here, we use a configuration of the Unified Model of the Met Office coupled to an ocean mixed layer model to investigate the role of upper-ocean features in the BoB on southwest monsoon precipitation. We focus on the pronounced zonal and meridional SST gradients characteristic of the BoB; the zonal gradient in particular has an as-yet unknown effect on monsoon rainfall. We find that the zonal SST gradient is responsible for a 50% decrease in rainfall over the southern BoB (approximately 5 mm day−1), and a 50% increase in rainfall over Bangladesh and northern India (approximately 1 mm day−1). This increase is remotely forced by a strengthening of the monsoon Hadley circulation. The meridional SST gradient acts to decrease precipitation over the BoB itself, similarly to the zonal SST gradient, but does not have comparable effects over land. The impacts of barrier layers and high-salinity subsurface water are also investigated, but neither has significant effects on monsoon precipitation in this model; the influence of barrier layers on precipitation is felt in the months after the southwest monsoon. Models should accurately represent oceanic processes that directly influence BoB SST, such as the BoB cold pool, in order to faithfully represent monsoon rainfall. [ABSTRACT FROM AUTHOR]

This paper describes the projection of climate change scenarios under increased greenhouse gas emissions, using the results of atmospheric-ocean general circulation models in the Coupled Model Intercomparison Project phase 3 dataset. A score is given to every model based on global and regional performance. Four out of 20 general circulation models (GCMs) were selected based on skill in predicting observed annual temperature and precipitation conditions. The ensemble of these four models shows superiority over the individual model scores. These models were subjected to increases in future anthropogenic radiative forcings for constructing climate change scenarios. Future climate scenarios for Tamil Nadu were developed with MAGICC/SCENGEN software. Model results show both temperature and precipitation increases under increased greenhouse gas scenarios. Northeast and northwest parts of Tamil Nadu show a greater increase in temperature and precipitation. Seasonally, the maximum rise in temperature occurred during the MAM season, followed by DJF, JJA, and SON. Decreasing trends of precipitation were observed during DJF and MAM. [ABSTRACT FROM AUTHOR]

This paper presents an approach to model the expected impacts of climate change on irrigation water demand in a reservoir command area. A statistical downscaling model and an evapotranspiration model are used with a general circulation model (GCM) output to predict the anticipated change in the monthly irrigation water requirement of a crop. Specifically, we quantify the likely changes in irrigation water demands at a location in the command area, as a response to the projected changes in precipitation and evapotranspiration at that location. Statistical downscaling with a canonical correlation analysis is carried out to develop the future scenarios of meteorological variables (rainfall, relative humidity (RH), wind speed ( U2), radiation, maximum (Tmax) and minimum (Tmin) temperatures) starting with simulations provided by a GCM for a specified emission scenario. The medium resolution Model for Interdisciplinary Research on Climate GCM is used with the A1B scenario, to assess the likely changes in irrigation demands for paddy, sugarcane, permanent garden and semidry crops over the command area of Bhadra reservoir, India. Results from the downscaling model suggest that the monthly rainfall is likely to increase in the reservoir command area. RH, Tmax and Tmin are also projected to increase with small changes in U2. Consequently, the reference evapotranspiration, modeled by the Penman-Monteith equation, is predicted to increase. The irrigation requirements are assessed on monthly scale at nine selected locations encompassing the Bhadra reservoir command area. The irrigation requirements are projected to increase, in most cases, suggesting that the effect of projected increase in rainfall on the irrigation demands is offset by the effect due to projected increase/change in other meteorological variables (viz., Tmax and Tmin, solar radiation, RH and U2). The irrigation demand assessment study carried out at a river basin will be useful for future irrigation management systems. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Climate change impact on a groundwater-dependent small urban town has been investigated in the semiarid hard rock aquifer in southern India. A distributed groundwater model was used to simulate the groundwater levels in the study region for the projected future rainfall (2012-32) obtained from a general circulation model (GCM) to estimate the impacts of climate change and management practices on groundwater system. Management practices were based on the human-induced changes on the urban infrastructure such as reduced recharge from the lakes, reduced recharge from water and wastewater utility due to an operational and functioning underground drainage system, and additional water extracted by the water utility for domestic purposes. An assessment of impacts on the groundwater levels was carried out by calibrating a groundwater model using comprehensive data gathered during the period 2008-11 and then simulating the future groundwater level changes using rainfall from six GCMs [Institute of Numerical Mathematics Coupled Model, version 3.0 (INM-CM.3.0); L'Institut Pierre-Simon Laplace Coupled Model, version 4 (IPSL-CM4); Model for Interdisciplinary Research on Climate, version 3.2 (MIROC3.2); ECHAM and the global Hamburg Ocean Primitive Equation (ECHO-G); Hadley Centre Coupled Model, version 3 (HadCM3); and Hadley Centre Global Environment Model, version 1 (HadGEM1)] that were found to show good correlation to the historical rainfall in the study area. The model results for the present condition indicate that the annual average discharge (sum of pumping and natural groundwater outflow) was marginally or moderately higher at various locations than the recharge and further the recharge is aided from the recharge from the lakes. Model simulations showed that groundwater levels were vulnerable to the GCM rainfall and a scenario of moderate reduction in recharge from lakes. Hence, it is important to sustain the induced recharge from lakes by ensuring that sufficient runoff water flows to these lakes. [ABSTRACT FROM AUTHOR]

Climate change information required for impact studies is of a much finer scale than that provided by Global circulation models (GCMs). This paper presents an application of partial least squares (PLS) regression for downscaling GCMs output. Statistical downscaling models were developed using PLS regression for simultaneous downscaling of mean monthly maximum and minimum temperatures ( T and T) as well as pan evaporation to lake-basin scale in an arid region in India. The data used for evaluation were extracted from the NCEP/NCAR reanalysis dataset for the period 1948-2000 and the simulations from the third-generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1, and COMMIT for the period 2001-2100. A simple multiplicative shift was used for correcting predictand values. The results demonstrated that the downscaling method was able to capture the relationship between the premises and the response. The analysis of downscaling models reveals that (1) the correlation coefficient for downscaled versus observed mean maximum temperature, mean minimum temperature, and pan evaporation was 0.94, 0.96, and 0.89, respectively; (2) an increasing trend is observed for T and T for A1B, A2, and B1 scenarios, whereas no trend is discerned with the COMMIT scenario; and (3) there was no trend observed in pan evaporation. In COMMIT scenario, atmospheric CO concentrations are held at year 2000 levels. Furthermore, a comparison with neural network technique shows the efficiency of PLS regression method. [ABSTRACT FROM AUTHOR]

This paper examines an issue concerning the simulation of anomalously wet Indian summer monsoons like 1994 which co-occurred with strong positive Indian Ocean Dipole (IOD) conditions in the tropical Indian Ocean. Contrary to observations it has been noticed that standalone atmospheric general circulation models (AGCM) forced with observed SST boundary condition, consistently depicted a decrease of the summer monsoon rainfall during 1994 over the Indian region. Given the ocean-atmosphere coupling during IOD events, we have examined whether the failure of standalone AGCM simulations in capturing wet Indian monsoons like 1994 can be remedied by including a simple form of coupling that allows the monsoon circulation to dynamically interact with the IOD anomalies. With this view, we have performed a suite of simulations by coupling an AGCM to a slab-ocean model with spatially varying mixed-layer-depth (MLD) specified from observations for the 1994 IOD; as well as four other cases (1983, 1997, 2006, 2007). The specification of spatially varying MLD from observations allows us to constrain the model to observed IOD conditions. It is seen that the inclusion of coupling significantly improves the large-scale circulation response by strengthening the monsoon cross-equatorial flow; leading to precipitation enhancement over the subcontinent and rainfall decrease over south-eastern tropical Indian Ocean-in a manner broadly consistent with observations. A plausible physical mechanism is suggested to explain the monsoonal response in the coupled frame-work. These results warrant the need for improved monsoon simulations with fully coupled models to be able to better capture the observed monsoon interannual variability. [ABSTRACT FROM AUTHOR]

Soil water‐groundwater interactions are important in determining the moisture profile in a soil column. However, the Noah Land Surface Model (Noah LSM), the land surface component of several general circulation models, does not consider groundwater effects. The present study investigates the impact of integrating a groundwater model into the Noah LSM. By performing experiments forced by observations at the surface, insights into the characteristics of soil moisture evolution with and without the aquifer are investigated. In the absence of surface precipitation, soil moisture is shown to exhibit an exponential decay rate without the aquifer (termed as Noah‐Cntl). This is because evapotranspiration dominates other processes governing the soil moisture evolution. Based on scale analysis, we derived an analytical equation that could well represent the aforementioned exponential variation in soil moisture. Presence of the aquifer (termed as Noah‐GW) makes the capillary processes important as well. The result is a slower decay of soil moisture as compared to Noah‐Cntl without surface precipitation. Soil moisture evolution in both the models is a function of vegetation and soil types. As a result, the aforementioned decay timescale displays significant heterogeneity over the Indian region, with Central India exhibiting relatively faster decay rates. We also performed 24‐year long experiments with observed interannually varying forcing that indicates an enhancement in the annual mean soil moisture at all levels due to groundwater effects. This enhancement is particularly prominent during the post‐monsoon season, reaffirming the results of the drain‐out experiments mentioned above. Plain Language Summary: Land surface processes are integral parts of weather and climate. Hence, an accurate representation of these processes is crucial from prediction and understanding points of view. However, state‐of‐the‐art land surface models do not incorporate some essential components that make these models erroneous. This study integrates a groundwater model into an existing land surface model and characterizes its impact on the evolution of soil moisture. We discuss factors affecting soil moisture decay rate over the Indian subcontinent without surface forcing. We show that the post‐monsoon and annual mean soil moisture increases in the presence of a groundwater model. However, spatial heterogeneity of groundwater effects is dependent on parameters like soil type and surface rain rate. Key Points: A prognostic model for groundwater is developed and integrated inside the Noah Land Surface ModelAnalytical solutions for the rate of change of soil moisture in the absence of surface water forcing are derived to explain model resultsPresence of an aquifer increases the soil moisture retention capability in the Noah Land surface model [ABSTRACT FROM AUTHOR]

Climate change is linked with change in precipitation, evapotranspiration, and other climatological parameters, and therefore the runoff of a river basin will be affected. The Gomati River basin is the largest in Tripura. The increased settlement in the Gomati River basin and climate change may threaten the natural flow patterns that enable its diversity. This study assesses the impact of climate change on total flow from a catchment in northeast India (the Gomati River catchment). For this assessment, the Group Method of Data Handling (GMDH) model was used to simulate the rainfall–runoff relationship in the catchment with respect to the observed data during 2008–2009. The statistically downscaled outputs of the Hadley Centre Global Environment Model version 2 (HadGEM2-ES) general circulation model scenario was used to assess the impacts of climate change on the Gomati River basin. Future projections were developed for the 2030s, 2040s, and 2050s. The results of this study may contribute to the development of adaptive strategies and future policies for the sustainable management of water resources in northeast Tripura. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Given the interdependence of climate change (CLC) and urban expansion (URE) on ecosystem productivity in China and India, hybrid physics-based models were fitted in this study to evaluate the effects of these variables on photosynthetically active radiation (PAR). This was accomplished by interpolating the most recent five general circulation models (GCMs) from coupled model intercomparison project phase 6 (CMIP6) into the CMIP6 multi-ensemble model. The potential of PAR is projected to increase by 0.001 to 2.077% in China and by 0.002 to 6.737% in India, on a seasonal and annual basis, if the warming is kept at 1.5 °C from now until the end of this century. The effects of CLC and URE on the changes in PAR in China and India were investigated, and URE had a greater impact than CLC when compared to effective contributions, with 49.47% for China and 28.41% for India in the entire case scenario. In contrast, CLC and PAR residual factor (PRF) have a greater impact in India than in China, with effects of 13.79% and 57.79% compared to 0.89% and 49.64%, respectively. Preferences for exotic, high-productivity plant species, irrigation, CO2 fertilization, and nitrogen deposition are suggested as measures for replenishing PAR in both countries. [ABSTRACT FROM AUTHOR]

Stable water isotopes in ground-level vapour are key to estimating water exchange between geospheres. Their sampling, however, is limited to laser-absorption spectrometers and satellite observations, having inherent shortcomings. This study investigates diffusive kinetic fractionation during liquid condensation under supersaturated environment, providing a cost-effective, reliable way of sampling ground-level vapour isotopes (18O, 2H). Experiments were undertaken at three locations in India with 'liquid' samples collected from condensation of ambient air at 0°C. Simultaneously, pristine 'vapour' was sampled via cryogenic-trapping using liquid nitrogen–alcohol slush at –78°C. The 'liquid' condensed under supersaturation was progressively more depleted in 18O, and less enriched in 2H than expected under equilibrium fractionation, with an increasing degree of supersaturation expressed as saturation index (Si). This study revealed: (1) Si, molecular density, Rh, T together control the extent of isotopic kinetic fractionation. (2) The presence of diffusive concentration gradient inhibits the flow of heavier isotopes during liquid condensation. (3) The stochastic nature of the process cannot be explained using a physics-based model alone. The artificial neural network model is hence deployed to sample δ18O (δ2H) within –0.24 ± 1.79‰ (0.53 ± 11.23 ‰) of true value. (4) The approach can be extended to ground-validate isotope-enabled general circulation models and satellite observations. [ABSTRACT FROM AUTHOR]

This study uses the 30 General Circulation Models (GCMs) from the Coupled Model Intercomparison Project phase-6 (CMIP6) to examine the simulations of the surplus/deficit Indian summer monsoon rainfall (ISMR) and its associated air-sea interactions on intraseasonal to interannual timescales. The majority of the CMIP6 models simulate the seasonal mean state of ISMR over the Indian mainland with systematic biases. Best performing models (BPM; AWI-ESM-1-1-LR, BCC-CSM2-MR, BCC-ESM1, CNRM-CM6-1, CNRM-ESM2-1, GFDL-CM4, INM-CM5-0, MIROC-ES2L, MIROC6, TaiESM1) well simulated the seasonal mean precipitation with Taylor skill score > 0.75 and normalized root mean square error (NRMSE) is < 0.7. However, the models are failed to simulate precipitation over the orographic regions (Western Ghats). Improving the simulations of low-level winds and sea surface temperature (SST) with high spatial resolutions would provide better precipitation simulations. B-MME (multimodel ensemble mean of BPM) can capture the negative IOD-like (Indian Ocean Dipole) pattern during deficit monsoon years and fail to capture the positive IOD-like pattern during surplus monsoon years. Models overestimate the moisture transport from the West Indian Ocean to the sub-continent of India during deficit monsoons, which plays a crucial role in modulating the precipitation and its associated intraseasonal variability. The present analysis identified that during deficit monsoon years, the faster moving 20–100 days oscillations are evident; however, these oscillations are sluggish during surplus monsoon years, which affects the duration of convection activity and causes dry conditions over the regions. During surplus monsoon years, the Bay of Bengal (Arabian Sea) responds strongly (slowly) to the atmosphere than the deficit monsoon years. However, models are fail to represent the ocean's response to the atmosphere over the Bay of Bengal. The freshwater forcing improvement in the models simulates the ocean to atmosphere response over the Indian region. The present study further suggests that the improved simulation of the Indian summer monsoon (ISM) variability by the GCMs is possible by improving the ocean and atmosphere feedback mechanisms, sensitivities of the models among internal variables, and orographic features necessary for the accurate simulation of intraseasonal variability. [ABSTRACT FROM AUTHOR]

Climate change is a terrible global concern and one of the greatest future threats to societal development as a whole. The accelerating pace of climate change is becoming a major challenge for agricultural production and food security everywhere. The present study uses the midcentury climate derived from the ensemble of 29 general circulation models (GCMs) on a spatial grid to quantify the anticipated climate change impacts on rice, maize, black gram, and red gram productivity over Tamil Nadu state in India under RCP 4.5 and RCP 8.5 scenarios. The future climate projections show an unequivocal increase of annual maximum temperature varying from 0.9 to 2.2°C for RCP 4.5 and 1.4 to 2.7°C in RCP 8.5 scenario by midcentury, centered around 2055 compared to baseline (1981–2020). The projected rise in minimum temperature ranges from 1.0 to 2.2°C with RCP 4.5 and 1.8 to 2.7°C under RCP 8.5 scenario. Among the monsoons, the southwest monsoon (SWM) is expected to be warmer than the northeast monsoon (NEM). Annual rainfall is predicted to increase up to 20% under RCP 4.5 scenario in two-third of the area over Tamil Nadu. Similarly, RCP 8.5 scenario indicates the possibility of an increase in rainfall in the midcentury with higher magnitude than RCP 4.5. Both SWM and NEM seasons are expected to receive higher rainfall during midcentury under RCP 4.5 and RCP 8.5 than the baseline. In the midcentury, climate change is likely to pose a negative impact on the productivity of rice, maize, black gram, and red gram with both RCP 4.5 and RCP 8.5 scenarios in most places of Tamil Nadu. The magnitude of the decline in yield of all four crops would be more with RCP 8.5 over RCP 4.5 scenario in Tamil Nadu. Future climate projections made through multi-climate model ensemble could increase the plausibility of future climate change impact assessment on crop productivity. The adverse effects of climate change on cereal and legume crop productivity entail the potential adaptation options to ensure food security. [ABSTRACT FROM AUTHOR]

Since the impact of future climate change on wheat productivity is inconsistent, we studied geographic distribution and yield of wheat using two global General Circulation Models (GCMs) and Free Air CO2/O3 Enrichment (FACE) experiments. The GCMs (IPSL-CM5A-LR and NIMR-HADGEM2-AO) with four Representative Concentration Pathways (RCPs) and 19 bioclimatic variables were used for distribution/ecological niche modeling (ENM). Currently cultivated eight wheat cultivars were exposed to individual treatment of (i) ambient CO2, temperature, and ozone (ACO + AO + AT) representing the present climate scenario, and (ii) elevated CO2 (550 ppm) (ECO), (iii) elevated temperature (+ 2 °C) (ET), (iv) elevated O3 (ambient + 20 ppb) (EO), (v) elevated CO2 + elevated O3 (ECO + EO), and (vi) elevated CO2 + elevated temperature + elevated O3 (ECO + EO + ET) under FACE facility simulating the future climate change scenarios in 2050. The niche models predicted a reduction in climatically suitable areas for wheat, and identified "maximum temperature" as the most influencing factor for area reduction. The elevated CO2, O3, and temperature individually and in combinations had differential impacts on the yield of wheat cultivars. Only two cultivars, viz., DBW 184 and DBW 187 did not exhibit yield decline suggesting their suitability in the future climate change scenario. Since the performance of six out of eight cultivars significantly declined under simulated FACE experiment, and ENM predicted reduction in wheat cultivation area under RCP 8.5 in 2050, it was concluded that future of wheat cultivation in India is bleak. The study further indicates that coupling of bioclimatic modeling and FACE experiment can effectively predict the impact of climate change on different crops. [ABSTRACT FROM AUTHOR]

This study presents an approach for modelling and mapping fluvial flooding, considering both land use/land cover (LULC) change and climate change, and applies it to the Brahmani River Basin in eastern India. Climate change projections were obtained from the Coupled Model Intercomparison Project Phase 6 (CMIP6), and their impacts on the hydrology of the catchment were investigated using HEC-HMS and HEC-RAS software. Results reveal that changes in LULC types, specifically an increase in proportions of agricultural and built-up areas and a decrease in forest cover, as undergone between years 1985 and 2018, have increased peak discharge following a storm, thereby causing an increase in spatial extent of floods of different return periods. Moreover, downscaled climate change scenarios from two General Circulation Models were used to determine potential changes in river discharge according to two GHG emission scenarios from the latest IPCC: SSP245 and SSP585. The projections indicate that peak discharge and the spatial extent of flooded areas will increase for floods with return periods ranging from two to 100 years. This study demonstrates the important influence that changes in LULC have had on the susceptibility of the BRB to flooding, with climate change projected to further enhance the risk of flooding as the century progresses. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive analysis on the past and future changes in precipitation extremes and quantifies the relative contributions from its frequency and intensity across India, considering five extremeness levels, denoted by 95th, 99th, 99.9th, 99.95th, and 99.97th percentile. Gridded station‐based observations from the historical period (1951–2020) and simulations from 14 General Circulation Models for the future (2021–2100), participating in the Coupled Model Intercomparison Project phase 6 (CMIP6) are considered. Apart from an overall increasing pattern of precipitation extremes, it is noticed that the contribution of frequency dominates over intensity. Specifically, the frequency of 99th percentile daily rainfall is projected to increase approximately by two‐ (SSP245) to three‐ (SSP585) times in future. We also proposed a new zoning of entire Indian mainland, identified as six Homogeneous Precipitation Zones (HPZs). HPZ‐wise analysis reveals that the increase in frequency dominates over intensity for all the HPZs with a varying extent. For instance, increase in frequency is more for the climatologically high‐precipitation zones (HPZ‐3: Western Ghats, and HPZ‐6: North‐east India), whereas increase in intensity is more for the low‐precipitation zones (HPZ‐1: North‐west India, and HPZ‐4: Peninsular India). The degree of increase gets even more pronounced under the worst scenario SSP585, indicating a potential impact of anthropogenic activities on changing precipitation extremes. Findings of this study should be accounted in the climate change adaptation and mitigation strategies for future. Plain Language Summary: There are different characteristics of extreme precipitation, such as how frequent they occur, its intensity. Owing to change in climate, these characteristics are changing as time passes, as well as from one location to another location. How has it been changed in the past and how will it be changing in future? Is there any link with the nature of rainfall in a region? These are the focus of this study. We considered entire Indian mainland as our study domain that spans across a wide range of climatology. Overall, it is noted that both frequency and intensity are increasing and it is going to increase further in future. Moreover, frequency is increasing more rapidly than intensity. However, this finding varies from region to region. The regions that receive high precipitation, in general, may experience maximum increase in frequency (northeast and western ghats region in India), whereas the regions with relatively low precipitation may experience maximum increase in intensity (southern peninsular India). Such findings are expected to be useful in many applications related to climate change impacts on water sector. Key Points: Both frequency and intensity of extreme precipitation are increasing with spatial variation and it is going to increase further in futureIn general, change in the characteristics of precipitation extremes is dominated by increase in frequency rather than intensityHigh precipitation regions experience an increase in frequency and low precipitation regions experience an increase in intensity [ABSTRACT FROM AUTHOR]

By using multiple data sources and numerical simulation results from the atmospheric general circulation model (AGCM) of CAM4.0 (Community Atmosphere Model, version 4), we investigated the effect of thermal forcing over the Tibetan Plateau (TP) on the onset of the summer monsoon in the Arabian Sea (AS) and India. Results indicate that the spatial distribution of diabatic heating over the TP in May is a southeastern–northwestern inverse pattern. This diabatic heating shows a robust negative relationship with the onset date of the summer monsoon over the AS and India. A positive diabatic heating seesaw pattern can enhance the ascending (descending) motion over the southeastern (northwestern) TP, corresponding to above (below) normal in- situ precipitation. Temperature budget diagnosis reveals that the adiabatic heating due to anomalous vertical motion and relevant horizontal advection of temperature in the mid-upper troposphere are responsible for the warming over the TP. Consequently, the reversal of the meridional temperature gradient from winter to summer over South Asian regions occurs earlier than the climate-mean state. Furthermore, the diabatic heating over the TP induces an enhanced and westward-extended South Asian High (SAH), which together with the easterly along the southern flank of the SAH superimpose on the low-level westerly flow over the AS and India, results in intensive upper-level divergence-pumping and upward motion. This anomalous circulation configuration in lower and upper levels further facilitates an earlier onset of the summer monsoon in AS and India. These findings are corroborated in the sensitivity runs based on CAM4.0. [ABSTRACT FROM AUTHOR]

Atmospheric aerosols or atmospheric particulate matter affects climate variables like temperature and rainfall, agricultural productivity, soil, and human health. We evaluated aerosol lifecycle over India via simulations (2005–2014) from three general circulation models under the COALESCE project (carbonaceous aerosol emissions, source apportionment, and climate impacts; Venkataraman et al., 2020, 10.1175/bams‐d‐19‐0030.1). The ECHAM6.3‐HAM2.3, CAM5.3, and NICAM‐SPRINTARS simulations use identical regional emissions (from the Speciated Multi‐pollutant generator, SMoG‐India‐v1). Satisfactory model simulations of meteorological variable magnitudes and seasonal cycle have been achieved partly from the adoption of nudging. Estimations of anthropogenic aerosol, aerosol optical depth (AOD), and particulate matter surface concentrations are significantly improved from (a) dust tuning (b) use of satellite‐derived organic aerosol to carbon ratio, and (c) nudged meteorology to capture variables influencing the production of secondary sulfate. Larger wintertime under prediction (−30% to −60%) results from over prediction of seasonal planetary boundary layer height and the absence of secondary ammonium nitrate and organic aerosols. Vertical dispersion to higher altitudes than in observations calls for improved modeling of vertical mass flux representation. Carbonaceous aerosol residence time and AOD fraction larger than global mean values in India, with a seasonal predominance in the autumn and winter seasons can be explained by enhanced regional emissions from residential biofuel cooking, agricultural stubble burning, and traditional informal industries like brick production. Plain Language Summary: Air pollution, climate change and agricultural productivity are all affected by atmospheric pollutants called aerosols. Models must accurately estimate aerosol particle amounts seasonally, spatially and with altitude to capture their impacts. In this work, we use test and identify approaches to improve aerosol estimation by global climate models over India. We diagnose reasons for model disagreement with observations and suggest some ways to improve them. Larger abundances of carbonaceous aerosols (black carbon or soot, which causes warming and organic aerosol, which has high toxicity) are found over India, than globally, especially in autumn and winter. These result from enhanced regional emissions from residential biofuel cooking, agricultural stubble burning, and traditional informal industries like brick production. They could cause possible near‐term climate warming over India, in contrast to consistent aerosol induced cooling in other world regions. Key Points: Simulated aerosol mass improves from use of a regional emissions inventory, organic aerosol to carbon ratio and dust tuningModel improvements are needed in estimation of vertical mass flux, secondary nitrate and organic aerosolLarger carbonaceous aerosol residence times and AOD fraction over India, than global estimates, result from larger regional emissions [ABSTRACT FROM AUTHOR]

Advancements in computational power have enabled general circulation models (GCMs) to simulate climate variables at a higher resolution. However, GCM outputs often deviate from the observed climatological data and therefore need bias correction (BC) before they are used for impact studies. While there are several BC methods, BCs considering frequency, intensity and distribution of rainfall are few. This study proposes a BC method which focuses on separately correcting the frequency, intensity and distribution of precipitation. This BC was performed on high-resolution daily precipitation simulations of Meteorological Research Institute-Atmospheric General Circulation Model Version 3.2 with a 20-km grid size (MRI-AGCM3-2-S) model which is part of Coupled Model Intercomparison Project Phase 6 (CMIP6) on Netravati basin, a tropical river basin in India. The historical rain gauge station data was considered for testing the effectiveness of the BC method applied. The quantile–quantile (Q–Q) plot, Taylor diagram, Nash–Sutcliffe efficiency (NSE), coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), percentage bias (PBIAS) and correlation coefficient (R) are employed for the evaluation of the BC method. Higher R and R2 and lower RMSE, MAE and PBIAS values were observed for the bias-corrected GCM data than raw simulation. The PBIAS reduced from 15.6 to 6% when BC was applied. The analysis suggested that the proposed method effectively corrects the bias in rainfall over the basin. Furthermore, an attempt has been made to analyse the trend of historical and future rainfall in the basin. The analysis revealed a declining trend of precipitation in monsoon months with the magnitude of 12.44 mm and 56.7 mm in the historical and future periods respectively. This study demonstrates that BC should be applied before the use of GCM simulated precipitation for any analysis or impact studies to improve the predictions. [ABSTRACT FROM AUTHOR]

The Paleocene‐Eocene Thermal Maximum (PETM) is associated with major extinctions in the deep ocean, and significant paleogeographic and ecological changes in surface ocean and terrestrial environments. However, the impact of the associated environmental change on shelf biota is less well understood. Here, we present a new PETM record of a low paleolatitude shallow‐marine carbonate platform from Meghalaya, NE India (eastern Tethys). The biotic assemblage was distinctly different to other Tethyan PETM records dominated by larger benthic foraminifera and calcareous algae both in the Paleocene and Eocene. A change in taxa and forms indicating deeper waters with a concurrent decrease in abundance of shallow water algae suggests a sea‐level rise during the onset of the PETM. The record is lacking the ecological change from corals to larger foraminiferal assemblages and the Lockhartia dominance, characteristic of several other sections in the Tethys. Comparison with a global circulation model (GCM) indicates high regional temperatures in the Thanetian which may have excluded corals from the region. Furthermore, the regional circulation pattern is isolating the site from the wider Paratethys. Our study highlights the need for a diverse global perspective on shallow‐marine response to the PETM and the strength of coupling data to global climate models for interpretation. Key Points: Shallow‐marine Paleocene‐Eocene Thermal Maximum (PETM) successions are rare; here, we presented from the low paleolatitude NE India (eastern Tethys)The absence of coral reefs in NE India, in contrast to other Tethyan records, was driven by very high temperaturesLinking biotic records of this section with climate modeling allow to interpret the biotic differences across the Tethyan region [ABSTRACT FROM AUTHOR]

Selection of an appropriate ensemble of General Circulation Models (GCMs) is vital to properly assess the impacts of climate change in a region. Several methods exist for the ranking and selection of GCMs. The present study compares the commonly-used Compromise Programming (CP) method and the recently-developed Global Performance Indicator (GPI) technique for ranking the GCMs. The ability of 20 GCMs to simulate the observed monthly rainfall over the period 1961–2005 in the Upper Godavari River basin in India is assessed, and a grid-wise analysis is performed. The SPAtial EFficiency (SPAEF) metric and Kling–Gupta Efficiency (KGE) are used as the performance indicators to evaluate the 20 GCMs. Further, both the CP method and the GPI technique are applied to integrate the values of the performance indicators into one. The group decision-making (GDM) approach is employed to make a collective decision about the rank of the 20 GCMs considering all the grids. The results from the CP method suggest that the best models are MPI-ESM-P, MPI-ESM-LR, and CNRM-CM5-2, whereas those from the GPI technique indicate that the best-performing GCMs are NorESM1-M, FIO-ESM, and MPI-ESM-LR. An analysis is also performed to examine whether the ranking of the GCMs identified based on the grid-wise analysis also holds true when the average rainfall over the entire basin is considered for ranking. To this end, the average rainfall value across all the 12 grids in the basin is used for the evaluation. The ensemble of GCMs identified by the grid-wise study of GCMs using the CP method provides better SPAEF and KGE values when compared to that using the GPI technique. These results suggest that the GCMs have to be evaluated at the individual grids, and then collective information has to be taken to identify the ensemble of the best-performing GCMs. [ABSTRACT FROM AUTHOR]

Multi-Model Ensembles (MMEs) are used for improving the performance of GCM simulations. This study evaluates the performance of MMEs of precipitation, maximum temperature and minimum temperature over a tropical river basin in India developed by various techniques like arithmetic mean, Multiple Linear Regression (MLR), Support Vector Machine (SVM), Extra Tree Regressor (ETR), Random Forest (RF) and long short-term memory (LSTM). The 21 General Circulation Models (GCMs) from National Aeronautics Space Administration (NASA) Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset and 13 GCMs of Coupled Model Inter-comparison Project, Phase 6 (CMIP6) are used for this purpose. The results of the study reveal that the application of a LSTM model for ensembling performs significantly better than models in the case of precipitation with a coefficient of determination (R2) value of 0.9. In case of temperature, all the machine learning (ML) methods showed equally good performance, with RF and LSTM performing consistently well in all the cases of temperature with R2 value ranging from 0.82 to 0.93. Hence, based on this study RF and LSTM methods are recommended for creation of MMEs in the basin. In general, all ML approaches performed better than mean ensemble approach. [ABSTRACT FROM AUTHOR]

Analysing three high resolution coupled model simulations for the Medieval Climate Anomaly (MCA) and Little Ice Age (LIA) regimes, available as the last millennium simulations of the PMIP3, we find that Northeastern Indian summer monsoon rainfall (NEISMR) did not change appreciably from the MCA to the LIA. This is in contrast to the signals in the rest of the Indian region. Our results from all the models suggest that, during the MCA, the simulated 100‐hPa tropical easterly jet (TEJ) becomes relatively more intense than that during LIA, due to a stronger Tibetan High. This strengthening of the TEJ results in an associated increase in relative vorticity at the 500 hPa over the Head Bay of Bengal and neighbouring east coast of the Bay of Bengal. This results in higher moisture availability and increased summer monsoon rainfall in the neighbourhood, including the central Indian region, during the MCA. However, the simulations do not show any such changes over northeastern India, indicating a relatively stable simulated NEISMR from MCA through LIA. Furthermore, just as the current day observations, the simulated correlations between the NEISMR with various concurrent ENSO indices are weak and statistically insignificant during the MCA and LIA regimes. Interestingly, an analysis of time‐slice simulations for the MCA and LIA from an atmospheric general circulation model broadly agree with the above conclusions, indicating that the tropical ocean and atmospheric coupling may not have played a major role in the northeast climate. [ABSTRACT FROM AUTHOR]

Abstract: The increasing global warming is most likely to affect the magnitude and pattern of wind at a regional level and such an effect may or not follow the trend predicted at the global scale. Regional level exercises are therefore necessary while making decisions related to engineering infrastructure. In this paper an attempt is made to know the extent of change in design as well as operational wind conditions at two offshore locations along the west coast of India. The design wind speeds with return periods of 10, 50 and 100 years derived for two 30-year time slices in the past and future are compared. In two separate exercises the past and future wind at the local level is simulated by empirical downscaling as well as by interpolation of the general circulation model (GCM) output. Both sets of past and future data are fitted to the Generalized Pareto as well as Weibull distributions using the peak over threshold method to extract long term wind speeds with a specified return. It is noticed that at the given locations the operational and design wind may undergo an increase of around 11%–14% when no downscaling is adopted and 14%–17% when the GCM data are downscaled. Although these figures may suffer from a certain level of statistical uncertainty the study points out to take a relook into the safety margins kept in the design and operation of ocean structures in the light of global warming. [Copyright &y& Elsevier]

Anthropogenic aerosol induced declining trends in Indian region precipitation are now acknowledged, however, the role of rapid adjustments to aerosol forcing needs further understanding. An atmospheric general circulation model with dynamic aerosol fields, ECHAM6-HAM2, is used to investigate changes in stratiform and convective precipitation through simulations using different levels of aerosol emissions over India. The spatial pattern of precipitation change, with increased aerosol levels, increases over the Northern Indian region, decreasing over peninsular south India. This is driven by the spatial heterogeneity of stratiform precipitation changes, while simultaneous convective precipitation changes are negative throughout the subcontinent, from absorbing aerosol induced stabilization. Stratiform response to the rapid adjustments includes dynamic changes to the divergence of the dry static energy, cloud microphysics and rainfall formation processes. Positive divergence in dry static energy, consistent with moisture convergence, leads to increased water vapour mixing ratios, thus significantly increasing cloud liquid water and cloud fraction in north India. The increases in rainfall formation processes like the autoconversion rate result from those in the liquid water path, which overcome decreases due to increased droplet number concentration. Overall, aerosol induced changes in dynamic dry static energy divergence, rather than on cloud microphysics, play a dominant role in driving the spatial heterogeneity in stratiform and total precipitation change in the Indian region. [ABSTRACT FROM AUTHOR]

Summary: A modeling framework is presented in this paper, integrating hydrologic scenarios projected from a General Circulation Model (GCM) with a water quality simulation model to quantify the future expected risk. Statistical downscaling with a Canonical Correlation Analysis (CCA) is carried out to develop the future scenarios of hydro-climate variables starting with simulations provided by a GCM. A Multiple Logistic Regression (MLR) is used to quantify the risk of Low Water Quality (LWQ) corresponding to a threshold quality level, by considering the streamflow and water temperature as explanatory variables. An Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) presented in an earlier study is then used to develop adaptive policies to address the projected water quality risks. Application of the proposed methodology is demonstrated with the case study of Tunga–Bhadra river in India. The results showed that the projected changes in the hydro-climate variables tend to diminish DO levels, thus increasing the future risk levels of LWQ. [Copyright &y& Elsevier]

Many studies based on observed data indicate the inverse relationship between the Eurasian snow cover/depth and the Indian summer monsoon rainfall (ISMR). The purpose of this study is to confirm the inverse snow-ISMR relationship by using the observed snow depth data as boundary conditions in the spectral general circulation model (GCM) of Indian Institute of Technology, Delhi (IITD), and to examine the influence of Eurasian snow depth on the monsoon circulation. The original model belonging to the European Centre for Medium range Weather Forecasts (ECMWF) at resolution T21 has been modified extensively to a higher resolution of T80L18 at IITD. A two-dimensional Lanczos digital filter has been used to represent the orography realistically. The Historical Soviet Daily Snow Depth (HSDSD) version II data set has been used for conducting sensitivity experiments using the above model. Two sensitivity experiments have been designed, corresponding to two contrasting cases: one with high Eurasian snow depth in spring followed by deficient ISMR and the second with low snow depth followed by excess ISMR. The difference fields of mean monsoon circulation simulated in the above two experiments are examined in detail in order to confirm the influence of Eurasian snow depth on ISMR and to examine the Asian summer monsoon circulation and rainfall. Copyright © 2006 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

The skill of General Circulation Models (GCMs) in mimicking the observed climate is assessed through various procedures and are ranked. The performance of a GCM is site‐specific and the ranking pattern varies spatially. In general, a set of best performing GCMs is extracted to study the impact of climate change. As, there is no universally accepted ranking procedure, the ranking of GCMs is prone to subjectivity. In this study, it is aimed to address the effect of this subjectivity on the GCM rankings. The past performance of GCMs from Coupled Model Intercomparison Project phase 6 (CMIP6) in simulating the maximum and minimum temperature across India are evaluated and ranked by different ranking procedures. These ranking procedures involve combinations of various components present in the ranking procedure such as model evaluation criteria, criteria weightage allocation methods, Multicriteria Decision Making methods (MCDM) and reference gridded datasets. Different criteria and methods involved in the ranking procedures are carefully selected to address the subjectivity involved in ranking of GCMs. The effect of each individual component on the ranking pattern is systematically analysed and the spatial distribution of grids with same ranking patterns across all the combinations are considered as grids with same ranking. The performance of best performing GCM is attributed to homogenous climatic zones and its corresponding topological features. An ensemble of frequently performing top five GCMs among 16 different ranking procedures are extracted for each climate zone as the most suitable set of GCMs. [ABSTRACT FROM AUTHOR]

Species distribution modeling has been an expedient tool. The optimization of species distribution models for Eastern Himalayan hill stream fishes remains a substantial challenge. This study has constructed a potentially informative species distribution model for an economically important and Near Threatened hill stream fish, Neolissochilus hexagonolepis. The wild habitats of this species along the Ganges–Brahmaputra basin of the Eastern Himalayas are under enormous threat. Initially, 21 predictor variables were selected based on their ecological relevance to stream fish distribution. The maximum entropy (MaxEnt) algorithm was employed to predict the habitat suitability in current and ensemble two future scenarios considering different global circulation models of climate change. Our results suggested that seasonality in annual temperature, precipitation, terrain wetness index, and eco-regional attributes had a higher contribution to the model. The fish species seems to be distributed in freshwater rivers, streams, and headwaters of lower (elevation < 500 m) to medium (elevation < 1000 m) elevation in north-eastern states of India along with other countries of Eastern Himalayas. A potential loss of current-suitable habitats is predicted in future scenarios along the lower altitudes (500 m < elevation < 1000 m). Country-wide shifts appear significant in the analysis of variance. The maximum habitat loss is projected in the sub-Himalayan belt of India and Bangladesh. The results also indicate that this species would tend to disperse towards higher altitudes and northward. Overall, this study emphasizes preserving dispersal connectivity and identifying crucial corridors for this commercially essential cold-water fish species. The stakes of becoming climate change winners for such fish species are low, considering successful colonization to upstream habitats tracking their habitat suitability. [ABSTRACT FROM AUTHOR]

Lightning activity constitute the major destructive component of thunderstorms over India. Hence, an understanding of the long-term variability in lightning occurrence and intensity and their interrelation with various causative factors is required. Long-term (1998–2014) Tropical Rainfall Measuring Mission (TRMM) satellite-based lightning observations depict the most frequent lightning occurrences along the Himalayan foothills, the Indo-Gangetic plains and coastal regions, while the intensity of these lightning strikes is found to be strongest along the coastal regions and in the Bay of Bengal. In addition, both of the abovementioned lightning properties show a very strong intensification (∼ 1 %–2.5 % annually) across all Indian regions during the 1998–2014 period with the maximum trends along the coasts. Accordingly, a detailed statistical dominance analysis is performed which reveals total column water vapor (TCWV) to be the dominant factor behind the intensification in lightning events, while instability, measured by the convective available potential energy (CAPE), and aerosol optical depth (AOD) jointly control the lightning frequency trends. An increase in surface temperatures has led to enhanced instability and, hence, stronger moisture transport to the upper-troposphere and lower-stratosphere regions, especially along the coasts. This transported moisture helps deplete the ozone concentration, leading to reduced temperatures and elevated equilibrium levels, which finally results in stronger and more frequent lightning events, as also evidenced by the trend analysis. Consequently, the relationships between lightning and its causative factors have been expressed in the form of multilinear regression equations, which are then employed in multiple global circulation models (GCMs) to understand the long-term impact of urbanization on lightning over the period from 1950 to 2100. The analysis reveals a uniform increase in lightning occurrence and intensity using both urbanization scenarios; however, accelerated growth is observed in the RCP8.5 projections after the year 2050, as also observed from the surface warming trends. As a result, lightning frequency and intensity values across the Indian region are expected to increase ∼ 10 %–25 % and 15 %–50 %, respectively, by the end of the century with the highest risk along the coasts; hence, this requires immediate attention from policymakers. [ABSTRACT FROM AUTHOR]

Landslide susceptibility assessment (LSA) is a method used to reduce landslide vulnerability defined as landslide spatial prediction with the help of associative factors. The goal of the analysis is to forecast potential (2040, 2060, 2080, and 2100 AC) rainfall and land use and land cover (LULC) with the aid of the CSIRO-MK3.6.0 General circulation models (GCMs) climatic model and the dynamic conversion of land use and its effects (Dyna-CLUE) model. The purpose of this work is to produce landslide susceptibility map (LSM) of potential cycles correlated with expected rainfall and LULC data utilizing the binary logistic regression (BLR) models in the Upper Rangit River basin of eastern Himalayan region, India. Including rainfall and LULC total nineteen factors have been incorporated, these are mainly topographical, hydrological, geological, and environmental factors. A total 671 landslide locations have been mapped which divided randomly as training (70%) and validation (30%) datasets of LSM. Current (2018) LSM was validated by 30% of landslide inventory location and the result of area under curve of receiver operating characteristic (ROC) indicated that the LSM has 92.4% and 89.6% of the success and prediction rate respectively. To demonstrate the accuracy of the BLR landslide susceptibility (LS) model, this model was used to generated future LSMs based on projected of rainfall and LULC. The result of projected representative concentration pathway (RCP)-based rainfall depicted that the increasing trend of rainfall in the future period and the moderate to very high LS zones has also increased from 2040 to 2100. This study will be used to further study of landslide hazard (LH) studies with climatic approaches, and will also contribute to regional planning and development of current and future Upper Rangit River basin. [ABSTRACT FROM AUTHOR]

Using an initial manifold approach, an ensemble forecast methodology is shown to simultaneously increase lead and realizable skill in long-range forecasting of monsoon over continental India. Initial manifold approach distinguishes the initial states that have coherence from a collection of unrelated states. In this work, an optimized and validated variable resolution general circulation model is being adopted for long-range forecasting of monsoon using the multi-lead ensemble methodology. In terms of realizable skill (as against potential) at resolution (~60km) and lead (2–5 months) considered here, the present method performs very well. The skill of the improved methodology is significant, capturing 9 of the 12 extreme years of monsoon during 1980–2003 in seasonal (June–August) scale. Eight-member ensemble-average hindcasts carried out for realizable skill with lead of 2 (for June) to 5 (for August) months and an optimum ensemble is presented. [ABSTRACT FROM AUTHOR]

The South Asian summer monsoon supplies over 80 % of India's precipitation. Industrialization over the past few decades has resulted in severe aerosol pollution in India. Understanding monsoonal sensitivity to aerosol emissions in general circulation models (GCMs) could improve predictability of observed future precipitation changes. The aims here are (1) to assess the role of aerosols in India's monsoon precipitation and (2) to determine the roles of local and regional emissions. For (1), we study the Precipitation Driver Response Model Intercomparison Project experiments. We find that the precipitation response to changes in black carbon is highly uncertain with a large intermodel spread due in part to model differences in simulating changes in cloud vertical profiles. Effects from sulfate are clearer; increased sulfate reduces Indian precipitation, a consistency through all of the models studied here. For (2), we study bespoke simulations, with reduced Chinese and/or Indian emissions in three GCMs. A significant increase in precipitation (up to ∼20 %) is found only when both countries' sulfur emissions are regulated, which has been driven in large part by dynamic shifts in the location of convective regions in India. These changes have the potential to restore a portion of the precipitation losses induced by sulfate forcing over the last few decades. [ABSTRACT FROM AUTHOR]

The positive Indian Ocean Dipole (IOD) event in 2019 was among the strongest on record, while the Indian Summer monsoon (ISM) was anomalously dry in June then very wet by September. We investigated the relationships between the IOD, Pacific sea surface temperature (SST), and ISM rainfall during 2019 with an atmospheric general circulation model forced by observed SST anomalies. The results show that the extremely positive IOD was conducive to a wetter‐than‐normal ISM, especially late in the season when the IOD strengthened and was associated with anomalous low‐level divergence over the eastern equatorial Indian Ocean and convergence over India. However, a warm SST anomaly in the central equatorial Pacific contributed to low‐level divergence and decreased rainfall over India in June. These results help to better understand the influence of the tropical SST anomalies on the seasonal evolution of ISM rainfall during extreme IOD events. Plain Language Summary: A prominent pattern of variability in the Indian Ocean is a seesaw in sea surface temperature (SST) between the eastern and western sides of the Ocean basin, called the Indian Ocean Dipole (IOD). Its influence on the regional weather and climate is not yet fully established, but the extremely strong IOD event in 2019 provided us the opportunity to consider its impact on the Indian Summer Monsoon. By simulating the response to the anomalous SST patterns that occurred in 2019, and by observation‐based analyses, we find evidence that the IOD did influence the monsoon rainfall in 2019, but that SST anomalies in the Pacific Ocean were also important. Our simulations show that the positive IOD was conducive to wetter‐than‐normal conditions throughout and especially at the end of the monsoon season, but that anomalous warmth in the central equatorial Pacific may have contributed to reduced rainfall in June over India. The results from this study help to understand the role of SST anomalies within and outside the Indian Ocean in affecting ISM rainfall intensity and seasonal evolution during extreme IOD events. Key Points: The positive Indian Ocean Dipole event that occurred in 2019 was among the strongest in the modern instrumental recordThe 2019 Indian Summer monsoon was drier than usual in June then wetter in the following months, resulting in above‐normal seasonal rainfallThe seasonal evolution of ISM was partly driven by a combination of equatorial Pacific and Indian Ocean sea surface temperature anomalies [ABSTRACT FROM AUTHOR]

A spatio-temporal analysis of representative concentration pathways (RCPs)-based projections of surface air temperatures under different combinations of global circulation models (GCMs) and RCPs for Satluj River Basin has been presented. The projections of temperature anomalies have been obtained for several meteorological stations in the basin using 16 different combinations of emission scenarios and global climate models (GCMs). For each combination, the projections of temperature anomalies have been analysed for two future time periods centred at 2030 and 2050. The results of the analysis conducted herein clearly indicate that the temperature will rise for all the future time scales with the maximum increase being projected under RCP8.5 compared to the baseline 1986–2005. However, there is large inter-model variability in the projections of temperature anomalies under different RCPs. Under RCP 8.5, the average temperature in Satluj basin is projected to rise by around 4°C by mid of the twenty-first century. The projections of temperature anomalies analysed herein could be potentially used for the evaluation of hydrological impacts of climate change in Satluj River Basin—a key basin in the Himalayan region from the view point of global warming. [ABSTRACT FROM AUTHOR]

Heat waves in India during the pre‐monsoon months have significant impacts on human health, productivity and mortality. While greenhouse gas‐induced global warming is believed to accentuate high temperature extremes, anthropogenic aerosols predominantly constituted by radiation‐scattering sulfate are believed to cause an overall cooling in most world regions. However, the Indian region is marked by an abundance of absorbing aerosols, such as black carbon (BC) and dust. The goal of this work was to understand the association between aerosols, particularly those that are absorbing in nature, and high‐temperature extremes in north‐central India during the pre‐monsoon season. We use 30‐year simulations from a chemistry‐coupled atmosphere‐only general circulation model (GCM), ECHAM6‐HAM2, forced with evolving aerosol emissions in an interactive aerosol module, along with observed evolving SSTs. A composite of high‐temperature extremes in the model simulations, compared to climatology, shows large‐scale conditions conducive to heat waves. Importantly, it reveals concurrent positive anomalies of BC and dust aerosol optical depths. Changes in near‐surface properties include a reduction in single scattering albedo (implying greater absorption) and enhancement in short‐wave heating rate, compared to climatological conditions. Alterations in surface energy balance include reduced latent heat flux, but increased sensible heat flux, consistent with enhanced temperatures. Thus, chemistry‐coupled GCM simulations capture an association of absorbing aerosols with high‐temperature extremes in north India, arising from radiative heating in the surface layer. [ABSTRACT FROM AUTHOR]

The future vegetation–climate system over East Asia, as well as its dependence on Representative Concentration Pathways (RCPs), is investigated using a regional climate–vegetation model driven with boundary conditions from Flexible Global Ocean–Atmosphere–Land System Model: Grid-point Version 2. Over most of the region, due to the rising CO2 concentration and climate changes, the model projects greater vegetation density (leaf area index) and gradual shifts of vegetation type from bare ground to grass or from grass to trees; the projected spatial extent of the vegetation shift increases from RCP2.6 to RCP8.5. Abrupt shifts are projected under RCP8.5 over northeast China (with grass replacing boreal needleleaf evergreen trees due to heat stress) and India (with tropical deciduous trees replacing grass due to increased water availability). The impact of vegetation feedback on future precipitation is relatively weak, while its impact on temperature is more evident, especially during DJF over northeast China and India with differing mechanisms. In northeast China, the projected forest loss induces a cooling through increased albedo, and daytime high temperature (Tmax) is influenced more than nighttime low temperature (Tmin); in India, increased vegetation cover induces an evaporative cooling that outweighs the warming effect of an albedo decrease in DJF, leading to a weaker impact on Tmax than on Tmin. Based on a single model, the qualitative aspects of these results may hold while quantitative assessment will benefit from a follow-up regional model ensemble study driven by multiple general circulation models. [ABSTRACT FROM AUTHOR]

This study investigated the impacts of changes in land cover and climate on runoff and sediment yield in a river basin in India. Land Change Modeler was used to derive the future land cover and its changes using the Sankey diagram approach. The future climatic parameters were derived from five general circulation models for two emission scenarios with representative concentration pathways (RCPs) 4.5 and 8.5. The land cover and climate change impacts on runoff and sediment yield were estimated using SWAT model. The results show important changes in land cover and indicate that urban and agricultural areas strongly influence the runoff and sediment yield. Among the land cover and climate change impacts, climate has more predominant (70%–95%) impact. Runoff and sediment yield are likely to decrease in both RCP scenarios in the future period. The impacts of land cover changes are more prominent on sediment yield than runoff. [ABSTRACT FROM AUTHOR]

Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) is one of the longest-serving observation platforms which provides the cloud fraction data with higher spatial and vertical resolutions. Their simulators are also incorporated in the General Circulation Models (GCMs) under the Coupled Model Intercomparison Project Phase 5 (CMIP5). Here, we have examined the spatial and vertical cloud fraction (CF) distributions and corresponding biases (CFmodel−CFobservations; CF bias) from CALIPSO observations and their simulators for the period 2006–2015 over Indian land and oceanic regions for Indian summer monsoon season of June to September. It is reported that the CF biases over the whole Indian land and oceanic regions are largely negative (> 55%) except the northeastern land regions and southern Bay of Bengal (BOB) which show positive biases (~ 10–60%). Such CF biases are prominent and consistent in all CMIP5 models. The CF biases are further analyzed in the vertical dimension using contoured frequency by altitude diagrams (CFADs). The large CF biases exist at each vertical level especially at higher altitudes (> 12 km). CF biases over BOB based on spatial distributions give the false impression of better performance of simulators which are exhibited by the co-existence of both positive and negative biases at the same level, especially at higher altitude levels. As a result, both positive and negative CF biases cancel each other which lead to the minimum values of CF biases at respective altitude levels. This mutual ambiguity between spatial and vertical CF biases is found to be a permanent feature of the CALIPSO simulator. [ABSTRACT FROM AUTHOR]

The three-dimensional Massachusetts Institute of Technology general circulation model (MITgcm) with time-dependent forcing is implemented to investigate the spatial and temporal variability of internal tides over the eastern coast of India. Numerical simulations are conducted for a number of different months of the year for which in situ observations are available at high temporal resolution. During the months of November–December and March–April, peak spectral estimates of density variability are evident in the semidiurnal frequency band in both observations and simulations, while during August, variability is dominantly in the near-inertial frequency band. Empirical orthogonal functions (EOF) analysis is applied to decompose the baroclinic tidal currents into vertical modes. The results show that about 70–80% of the total variance is in the first mode, while the first three modes represent 90–95% of the total variance in all seasons. Internal tide characteristics such as phase speed, group speed, and wavelength are largest in the postmonsoon season. The magnitude of the computed energy flux is greatest during November, while the direction of propagation of internal tides is almost unchanged through the year. The available potential energy peaks in November (20 kJ m−2) and is smallest in August (14 kJ m−2). Calculation of the energy budget shows that the energy conversion rate from barotropic forcing to internal tides is about 85% in November but only about 46% in August. [ABSTRACT FROM AUTHOR]

The Met Office Unified Model (MetUM) is known to produce too little total rainfall on average over India during the summer monsoon period, when assessed for multiyear climate simulations. We investigate how quickly this dry bias appears by assessing the 5-day operational forecasts produced by the MetUM for six different years. It is found that the MetUM shows a drying tendency across the five days of the forecasts, for all of the six years (which correspond to two different model versions). We then calculate each term in the moisture budget, for a region covering southern and central India, where the dry bias is worst in both climate simulations and weather forecasts. By looking at how the terms vary with forecast lead time, we are able to identify biases in the weather forecasts that have been previously identified in climate simulations using the same model, and we attempt to quantify how these biases lead to a reduction in total rainfall. In particular, an anticyclonic bias develops to the east of India throughout the forecast, and it has a complex effect on the moisture available over the peninsula, and a reduction in the wind speed into the west of the region appears after about 3 days, indicative of upstream effects. In addition, we find a new bias that the air advected from the west is too dry from very early in the forecast, and this has an important effect on the rainfall. [ABSTRACT FROM AUTHOR]

The decreasing trend in rainfall in the last few decades over the Indo-Gangetic Plains of northern India as observed in ground-based observations puts increasing stress on groundwater because irrigation uses up to 70% of freshwater resources. In this work, we have analyzed the effects of extensive irrigation over the Gangetic Plains on the seasonal mean and intra-seasonal variability of the Indian summer monsoon, using a general circulation model and a very high-resolution soil moisture dataset created using extensive field observations in a state-of-the-art hydrological model. We find that the winter-time (November–March) irrigation has a positive feedback on the Indian summer monsoon through large scale circulation changes. These changes are analogous to a positive North Atlantic Oscillation (NAO) phase during winter months. The effects of the positive NAO phase persist from winter to spring through widespread changes in surface conditions over western and central Asia, which makes the pre-monsoon conditions suitable for a subsequent good monsoon over India. Winter-time irrigation also resulted in a reduction of low frequency intra-seasonal variability over the Indian region during the monsoon season. However, when irrigation is practiced throughout the year, a decrease in June–September precipitation over the Gangetic Plains, significant at 95% level, is noted as compared to the no-irrigation scenario. This decrease is attributed to the increase in local soil moisture due to irrigation, which results in a southward shift of the moisture convergence zone during the active phase of monsoon, decreasing its mean and intraseasonal variability. Interestingly, these changes show a remarkable similarity to the long-term trend in observed rainfall spatial pattern and low-frequency variability. Our results suggest that with a decline in the mean summer precipitation and stressed groundwater resources in the Gangetic Plains, the water crisis could exacerbate, with irrigation having a weakening effect on the regional monsoon. [ABSTRACT FROM AUTHOR]