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Groundwater is an important resource for a multitude of purposes, and change in groundwater levels is an important factor in determining the increase and decrease of groundwater quantity in aquifers. This study has been carried out for Gomti River basin, where groundwater is the major drinking water source for the entire region. This paper evaluates the behavior of the groundwater levels using Visual MODFLOW under RCP 4.5 and RCP 8.5 scenarios. For modeling of groundwater flow, the entire study area was discretized into 108480 (X-direction) and 207090 (Y-direction) gridal network with each cell size of 1000 m × 1000 m. The model was calibrated for the period 2002 to 2008 and validated for the period 2009 to 2013 using data of 24 observation wells. During the calibration period of Visual MODFLOW, residual mean ranged between 0.658 and 0.268 m, and absolute residual mean ranged between 2.035 and 2.174 m. The calibrated Visual MODFLOW model was employed to evaluate the influence of climate change on future groundwater levels. Future groundwater recharge (2020–2030), obtained after the application of four (MIROC-ESM, MIROC-ESM-CHEM, MIROC MIROC5, and MOHC-HADGEM2-ES) global climate model (GCM) data into prior SWAT model, was applied to Visual MODFLOW. After applying, the projected future (2020–2030) trend in the groundwater levels was found continuously decreasing due to low groundwater recharge and excessive groundwater withdrawal in the basin in future. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of gridded rainfall datasets for precipitation detection and streamflow simulations in Indiaʼs Tungabhadra river basin. Sixteen precipitation datasets categorized under gauge-based, satellite-only, reanalysis, and gauge-adjusted datasets were compared statistically against the gridded Indian Meteorological Dataset (IMD) employing two categorical and three continuous statistical metrics. Further, the precipitation datasets' performance in simulating streamflow was assessed by using the Soil and Water Assessment Tool (SWAT) hydrological model. Based on the statistical metrics, Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) furnished very good results in terms of detecting rainfall, followed by Climate Hazards Group Infrared Precipitation (CHIRP), National Centres for Environmental Prediction-Climate Forecast System Reanalysis (NCEP CFSR), Tropical Rainfall Measurement Mission (TRMM) 3B42 v7, Global Satellite Mapping of Precipitation Gauge Reanalysis v6 (GSMaP_Gauge_RNL), and Multisource Weighted Ensemble Precipitation (MSWEP) datasets which had good-to-moderate performances at a monthly time step. From the hydrological simulations, TRMM 3B42 v7, CHIRP, CHIRPS 0.05°, and GSMaP_Gauge_RNL v6 produced very good results with a high degree of correlation to observed streamflow, while Soil Moisture 2 Rain-Climate Change Initiative (SM2RAIN-CCI) dataset exhibited poor performance. From the extreme flow event analysis, it was observed that CHIRP, TRMM 3B42 v7, Global Precipitation Climatology Centre v7 (GPCC), and APHRODITE datasets captured more peak flow events and hence can be further implemented for extreme event analysis. Overall, we found that TRMM 3B42 v7, CHIRP, and CHIRPS 0.05° datasets performed better than other datasets and can be used for hydrological modeling and climate change studies in similar topographic and climatic watersheds in India. [ABSTRACT FROM AUTHOR]

In recent times, India has experienced a significant increase in the frequency and intensity of extreme weather events, particularly in northeast India (NEI), an area known for its rich natural resources. Despite the geographic continuity of NEI and Bangladesh, previous studies have failed to consider their interconnectedness, resulting in an incomplete understanding of the situation. To bridge this gap, a comprehensive study encompassed the entire NEI, including West Bengal and Bangladesh (hereafter referred to as NEIB). This study examined climate extremes in NEIB, utilizing 12 temperature-based and 8 precipitation-based indices developed by the Expert Team on Climate Change Detection and Indices. Analysis was performed on temperature and precipitation data obtained from the India Meteorological Department and Bangladesh Meteorological Department covering the period 1981–2021. Additionally, climate projections from 14 Global Climate Models participating in the CMIP6 were incorporated for the period 2015–2100, considering four different Shared Socioeconomic Pathways (SSPs) scenarios. Findings revealed that under the SSP585 scenario, a substantial rise of 4 °C in maximum temperatures and 5.5 °C in minimum temperatures by the end of the twenty-first century. Warming indices, such as the summer days index, indicated an expected increase of 53 days, while the Warm spell days index was estimated to rise by approximately 2 days. Heavy precipitation days (R20mm) were projected to increase by up to 14 days, with a notable impact in Meghalaya. While the number of rainy days is expected to decrease, the overall magnitude of precipitation is anticipated to remain relatively stable. Notably, the Simple daily intensity index demonstrated a rise of 2.4 mm/day compared to the current baseline of 14.4 mm/day. These projected changes have significant ramifications for water resources, agriculture, health, and infrastructure in the region. [ABSTRACT FROM AUTHOR]

We studied the potential of using machine learning to downscale global-scale climate model output towards ground station data. The aim was to simultaneously analyze both city-level air quality and regional- and global-scale radiative forcing values for anthropogenic aerosols. As the city-level air pollution values are typically underestimated in global-scale models, we used a machine learning approach to downscale fine particulate (PM 2.5) concentrations towards measured values. We first simulated the global climate with the aerosol–climate model ECHAM-HAMMOZ and corrected the PM 2.5 values for the Indian megacity New Delhi. The downscaling procedure clearly improved the seasonal variation in the model data. The seasonal trends were much better captured in the corrected PM 2.5 than in original ECHAM-HAMMOZ PM 2.5 when compared to the reference PM 2.5 from the ground stations. However, short-term variations showed less extreme values with the downscaling approach. We applied the downscaling model also to simulations where the aerosol emissions were following two different future scenarios: one following the current legislation and one assuming currently maximum feasible emission reductions. The corrected PM 2.5 concentrations for the year 2030 showed that mitigating anthropogenic aerosols improves local air quality in New Delhi, with organic carbon reductions contributing most to these improvements. In addition, aerosol emission mitigation also resulted in negative radiative forcing values over most of India. This was mainly due to reductions in absorbing black carbon emissions. For the two future emission scenarios modeled, the radiative forcing due to aerosol–radiation interactions over India was -0.09±0.26 and -0.53±0.31 W m -2 , respectively, while the effective radiative forcing values were -2.1±4.6 and 0.06±3.39 W m -2 , respectively. Although accompanied by relatively large uncertainties, the obtained results indicate that aerosol mitigation could bring a double benefit in India: better air quality and decreased warming of the local climate. Our results demonstrate that downscaling and bias correction allow more versatile utilization of global-scale climate models. With the help of downscaling, global climate models can be used in applications where one aims to analyze both global and regional effects of policies related to mitigating anthropogenic emissions. [ABSTRACT FROM AUTHOR]

Representing subgrid variabilities of land surface processes and their upscaled effects is crucial for global climate modeling. Here, we implement a multiple atmosphere multiple land (MAML) framework in the superparamaterized version of E3SM (SP‐E3SM) to explicitly simulate the subgrid variabilities of land states and fluxes at cloud‐resolving scale and their interactions with atmosphere. Comparing to the standard SP‐E3SM in which all the atmospheric columns of the cloud resolving model embedded within the global atmospheric model grid interact with the same land surface (i.e., multiple atmosphere single land (MASL)), the impact of MAML on the strength of land‐atmosphere coupling is limited, partly because the current implementation mainly facilitates one‐way coupling between the cloud‐resolving model and the land surface model. Despite such limitation, MAML increases the surface latent heat flux at the expense of sensible heat flux, and increases precipitation in India, Amazon, and Central Africa, reducing the model dry bias compared to the standard SP‐E3SM. By employing a normalized gross moist stability (NGMS) diagnostic framework, we find that the increase in precipitation minus evaporation (P‐E) is primarily driven by the change in large‐scale moisture convergence, particularly by the increase of water vapor in the lower atmosphere, while the local effect of total surface energy flux plays a minor role in the P‐E change. More specifically, MAML changes the surface energy partitioning (evaporative fraction), increases the atmosphere water vapor, and further increases P‐E by decreasing the NGMS. Finally, future development in the MAML framework is discussed. Plain Language Summary: Land‐atmosphere interactions such as soil moisture‐precipitation feedback occur at a wide range of spatial scales. For example, spatial variability of surface fluxes such as radiation and precipitation can influence surface latent and sensible heat fluxes, which further influence turbulent mixing processes in the boundary layer and cloud and precipitation. Current global climate models (GCMs) have difficulties in representing land‐atmosphere interactions at scales smaller than the GCM grid (subgrid scales). To meet this challenge, we employ a novel framework, called multiple atmosphere multiple land (MAML), in a superparamaterized version of E3SM (SP‐E3SM) in which a cloud resolving model is embedded within each GCM grid to better resolve clouds and convection. MAML explicitly simulates the subgrid variabilities of land states and fluxes at scale of ∼1 km and provides feedback to the atmosphere. The use of MAML is shown to have a limited effect on the strength of land‐atmosphere coupling due to the current one‐way subgrid land‐atmosphere interaction setup. Despite the limited effect, MAML increases the rainfall in India, Amazon, and Central Africa, which reduces the dry bias in the model. Key Points: A multiple atmosphere multiple land (MAML) framework of land‐atmosphere coupling is implemented in the super‐parameterized E3SM (SP‐E3SM)MAML increases precipitation in India, Central Africa, and Amazon by increasing water vapor and hence large‐scale moisture convergenceMAML's impact on land‐atmosphere interactions is limited by the current setup of one‐way coupling on cloud‐resolving scales [ABSTRACT FROM AUTHOR]

This study examines the decadal change in the relationship between two major Indian Ocean (IO) sea surface temperature patterns, namely the Indian Ocean dipole (IOD) and northern IO and the East Asia summer monsoon (EASM) in the early 2000s. In 1991–1999, the former epoch, the interannual variability of EASM was associated with the IOD-like pattern in the original paper and its relationship weakened in 2000–2016. There are two possible causes for this decadal change; stronger land-sea thermal contrast as a local forcing in latter epoch, which may result in the weakening of the relationship between the IO and the EASM. In addition, the influence of El Niño-southern Oscillation (ENSO) on the western North Pacific subtropical high (WNPSH) could be changed depending on the frequency of ENSO. In the 2000s, the intensity of the low frequency (LF)-type ENSO (42–86 months period) events was weaker compared to the former epoch but that of quasi-biennial (QB)-type ENSO (16–36 months period) remained persistent. This could explain that the QB-type ENSO is remote forcing that modulates the change in the relationship between the tropical IO patterns and EASM in the 2000s. [ABSTRACT FROM AUTHOR]

The performance and validation of regional climate model (RegCM-4.3) simulation of Indian summer monsoon rainfall (ISMR) have been conducted with a futuristic view of climate change study with the convective parameterization schemes (CPSs) over the different homogeneous regions of India. The dynamical downscaling of RegCM-4.3 has been done over South Asian Coordinated Regional Climate Downscaling Experiment (CORDEX) domain with the lateral boundary forcing provided by ERA-Interim at 50-km horizontal resolution. The interannual and seasonal variability of ISMR over India and its different homogeneous region has been done by comparing model-simulated rainfall with observed rainfall dataset of India Meteorological Department (IMD) during 1986–2010. The analysis includes the performance and validation of RegCM-4.3 in capturing regionalized rainfall of Indian subcontinent. The analysis is done over five homogeneous regions of India, i.e., northwest India (NWI), R1; northcentral India (NCI), R2; west peninsular India (WPI), R3; eastern peninsular India (EPI), R4; and southern peninsular India (SPI), R5 during 1986–2010. The Grell CPSs simulate monsoon rainfall reasonably good over northwest India. Over northcentral India, predictability/simulation of Kuo and Grell are best performing parameterize scheme of RegCM-4.3. The western and eastern peninsular parts of India, i.e., R3 and R4 simulation of Emanuel and Mix99 schemes, are better respectively. The region consisting southern peninsular along with Western Ghats shows Tiedtke scheme, as the best simulated scheme of RegCM-4.3. The overall diversification of simulation depending upon the topographical difference of Indian subcontinent causes the regionalize difference in simulating monsoon rainfall over the Indian subcontinent. [ABSTRACT FROM AUTHOR]

The Himalayan Mountains are the source region of one of the world's largest supplies of freshwater. The changes in glacier melt may lead to droughts as well as floods in the Himalayan basins, which are vulnerable to hydrological changes. This study used an integrated glacio-hydrological model, the Glacier and Snow Melt – WASMOD model (GSM-WASMOD), for hydrological projections under 21st century climate change by two ensembles of four global climate models (GCMs) under two Representative Concentration Pathways (RCP4.5 and RCP8.5) and two bias-correction methods (i.e., the daily bias correction (DBC) and the local intensity scaling (LOCI)) in order to assess the future hydrological changes in the Himalayan Beas basin up to Pandoh Dam (upper Beas basin). Besides, the glacier extent loss during the 21st century was also investigated as part of the glacio-hydrological modeling as an ensemble simulation. In addition, a high-resolution WRF precipitation dataset suggested much heavier winter precipitation over the high-altitude ungauged area, which was used for precipitation correction in the study. The glacio-hydrological modeling shows that the glacier ablation accounted for about 5 % of the annual total runoff during 1986–2004 in this area. Under climate change, the temperature will increase by 1.8–2.8 ∘ C at the middle of the century (2046–2065), and by 2.3–5.4 ∘ C until the end of the century (2080–2099). It is very likely that the upper Beas basin will get warmer and wetter compared to the historical period. In this study, the glacier extent in the upper Beas basin is projected to decrease over the range of 63 %–87 % by the middle of the century and 89 %–100 % at the end of the century compared to the glacier extent in 2005. This loss in glacier area will in general result in a reduction in glacier discharge in the future, while the future streamflow is most likely to have a slight increase because of the increase in both precipitation and temperature under all the scenarios. However, there is widespread uncertainty regarding the changes in total discharge in the future, including the seasonality and magnitude. In general, the largest increase in river total discharge also has the largest spread. The uncertainty in future hydrological change is not only from GCMs, but also from the bias-correction methods and hydrological modeling. A decrease in discharge is found in July from DBC, while it is opposite for LOCI. Besides, there is a decrease in evaporation in September from DBC, which cannot be seen from LOCI. The study helps to understand the hydrological impacts of climate change in northern India and contributes to stakeholder and policymaker engagement in the management of future water resources in northern India. [ABSTRACT FROM AUTHOR]

The Narmada river basin is a highly regulated catchment in central India, supporting a population of over 16 million people. In such extensively modified hydrological systems, the influence of anthropogenic alterations is often underrepresented or excluded entirely by large-scale hydrological models. The Global Water Availability Assessment (GWAVA) model is applied to the Upper Narmada, with all major dams, water abstractions and irrigation command areas included, which allows for the development of a holistic methodology for the assessment of water resources in the basin. The model is driven with 17 Global Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble to assess the impact of climate change on water resources in the basin for the period 2031–2060. The study finds that the hydrological regime within the basin is likely to intensify over the next half-century as a result of future climate change, causing long-term increases in monsoon season flow across the Upper Narmada. Climate is expected to have little impact on dry season flows, in comparison to water demand intensification over the same period, which may lead to increased water stress in parts of the basin. [ABSTRACT FROM AUTHOR]