Assessing the impact of soil moisture-temperature coupling on temperature extremes over the Indian region

While previous model sensitivity studies have mainly focused on discerning the soil moisture-precipitation feedback processes over the Indian region, the present study investigates the impact of soil moisture-temperature (SM-T) coupling on the temperature extremes (ExT) using the high-resolution (~60 km) model simulations. These simulations include the control and soil moisture (SM) sensitivity experiments (DRY-SM and WET-SM) initialized by perturbing (decreasing/increasing) SM from the historical (HIST: 1951-2010) and future 4K warming (FUT: 2051-2100) control runs. The analysis identifies the transitional regions of north-central India (NCI) as the hotspot of strong SM-T coupling. Over NCI, the HIST experiment shows an occurrence of 4-5 extreme events per year, with an average duration of 5-6 days per event and intensity exceeding 46oC. Whereas, FUT estimates indicate relatively severe, long-lasting, and more frequent extreme events. The SM sensitivity experiments reveal the significant influence of SM-T coupling on the ExT over NCI in both historical and future climates. We find that the DRY-SM results in significant enhancement of frequency, duration and intensity of ExT, in contrast to WET-SM. We note that the difference between DRY-SM and WET-SM 50-year return value of the block maxima GEV fit can reach upto 1.25oC and 3oC for historical and future climate, respectively. The enhanced (reduced) extreme temperature conditions in DRY-SM (WET-SM) simulation are caused by the intensification (abridgement) of sensible heat flux by limiting (intensifying) available total energy for evaporative cooling due to faster (slower) dissipation of positive soil moisture anomalies (also called as soil moisture memory). In addition, the influence of SM on ExT over NCI is found to be larger during the post-monsoon season as compared to the pre-monsoon and monsoon seasons.Comment: 60 pages, 13 figure

Deep Learning Based Forecasting of Indian Summer Monsoon Rainfall

Accurate short range weather forecasting has significant implications for various sectors. Machine learning based approaches, e.g., deep learning, have gained popularity in this domain where the existing numerical weather prediction (NWP) models still have modest skill after a few days. Here we use a ConvLSTM network to develop a deep learning model for precipitation forecasting. The crux of the idea is to develop a forecasting model which involves convolution based feature selection and uses long term memory in the meteorological fields in conjunction with gradient based learning algorithm. Prior to using the input data, we explore various techniques to overcome dataset difficulties. We follow a strategic approach to deal with missing values and discuss the models fidelity to capture realistic precipitation. The model resolution used is (25 km). A comparison between 5 years of predicted data and corresponding observational records for 2 days lead time forecast show correlation coefficients of 0.67 and 0.42 for lead day 1 and 2 respectively. The patterns indicate higher correlation over the Western Ghats and Monsoon trough region (0.8 and 0.6 for lead day 1 and 2 respectively). Further, the model performance is evaluated based on skill scores, Mean Square Error, correlation coefficient and ROC curves. This study demonstrates that the adopted deep learning approach based only on a single precipitation variable, has a reasonable skill in the short range. Incorporating multivariable based deep learning has the potential to match or even better the short range precipitation forecasts based on the state of the art NWP models.Comment: 14 pages, 14 figures. The manuscript is under review with journal 'Transactions on Geoscience and Remote Sensing

Assessing the impacts of human interventions and climate change on fluvial flooding using CMIP6 data and GIS-based hydrologic and hydraulic models

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

Need for consumer pharmacovigilance in Nepal

In Nepal, reporting of adverse drug reactions (ADRs) occurs on a voluntary basis by doctors, pharmacists, nurses, health assistants, and other healthcare professionals. The country’s pharmacovigilance program is still in its infancy; it has limited coverage and underreporting is common. This major limitation could be reduced with consumer involvement. This report examines the necessity and benefits of consumer involvement in Nepal’s existing pharmacovigilance program, reflecting on existing examples of consumer pharmacovigilance in different countries to highlight the necessity for such a framework in Nepal

Understanding the soil water dynamics during excess and deficit rainfall conditions over the core monsoon zone of India

Observations of soil moisture (SM) during excess and deficit monsoon seasons between 2000 to 2021 present a unique opportunity to understand the soil water dynamics (SWD) over core monsoon zone (CMZ) of India. This study aims to analyse SWD by investigating the SM variability, SM memory (SMM), and the coupling between surface and subsurface SM levels. Particularly intriguing are instances of concurrent monsoonal extremes, which give rise to complex SWD patterns. Usually, it is noted that a depleted convective activity and persistence of higher temperatures during the pre-monsoon season leads to lower SM, while monsoon rains and post-monsoon showers support the prevalence of higher SM conditions. The long persistent dry spells during deficit monsoon years enhances the Bowen ratio (BR) due to the high sensible heat fluxes. On the other hand, the availability of large latent heat flux during excess monsoon and post-monsoon seasons tend to decrease the BR. This enhancement or reduction in BR is due to evapotranspiration (ET), which influences the SWD by modulating the surface—subsurface SM coupling. The surface and subsurface SM coupling analysis for CMZ exhibits significant distinction in the evolution of wet and dry extremes. SM variations and persistence time scale is used as an indicator of SMM, and analysed for both surface and subsurface SM observation levels. Evidently, subsurface SM exhibits remarkably prolonged memory timescales, approximately twice that of surface SM. Furthermore, we dissect SWD linked to wet and dry extremes by analysing annual soil water balance at a local site in Pune, India. Our findings reveal that ET and deep drainage on annual scale are modulated largely by number of break events during the monsoon season. In essence, our study underscores the significance of surface–subsurface SM observations in unravelling the intricate tapestry of SWD

Climatology, trends, and future projections of aerosol optical depth over the Middle East and North Africa region in CMIP6 models

This study assesses the aerosol optical depth (AOD) from historical simulations (2003–2014) and future climate scenarios (2015–2100) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) over the Middle East and North Africa (MENA) region. Multi-model mean (MME) AOD statistics are generated as the average of those from the five best-performing CMIP6 models, which reproduce observational climate statistics. These models were selected based on the validation of various climate metrics, including strong pattern correlations with observations (>0.8). The resulting MME reproduces the observed AOD seasonal cycle well. The observed positive trends (summer and annual) over the Arabian Peninsula (AP) and negative trends (winter) over North Africa are well captured by MME, as regional meteorological drivers associated with observed AOD trends, with few discrepancies. Crucially, the MME fails to capture the AOD trends over North West Africa (NWA). For MENA and NWA regions, two high-emission scenarios, SSP370 and SSP585, project a continuous rise in the annual mean AOD until the end of the century. In contrast, the low-emission scenarios, SSP126 and SSP245, project a decreasing AOD trend. Interestingly, the projected future AOD area-averaged over the AP region varies significantly across all four scenarios in time. Notably, a substantial decrease of about 8–10% in the AOD is projected by the SSP126, SSP245, and SSP585 scenarios at the end of the century (2080–2100) relative to the current period. This projected decrease in annual-mean AOD, including the frequency of extreme AOD years under SSP585, is potentially associated with a concurrent increase in annual-mean rainfall over the AP

Number of tillers in wheat is an easily measurable index of genotype tolerance to saline waterlogged soils: Evidence from 10 large-scale field trials in India

© 2018 CSIRO. Over 100 wheat varieties and breeding lines from India and Australia were screened in alkaline and waterlogged soils in 10 environments over two years at one drained location and two naturally waterlogged locations in India. Mean trial grain yield was reduced up to 70% in the environments where genotypes were waterlogged for up to 15 days at the vegetative stage in alkaline soil relative to plants in drained soils. Agronomic traits (plant height, tiller number, 1000-grain weight) of genotypes were also reduced under waterlogging. At one waterlogged site, up to 68% of the genetic diversity for predicted grain yields under waterlogging could be accounted for by number of tillers (r2= 0.41-0.68 in 2011 and 2010, respectively) and positive correlations also occurred at the second site (r2= 0.19-0.35). However, there was no correlation between grain yields across varieties under waterlogging in any trials at the two waterlogged locations. This may have occurred because waterlogged sites differed up to 4-fold in soil salinity. When salinity was accounted for, there was a good correlation across all environments (r 2 = 0.73). A physiological basis for the relationship between tillering and waterlogging tolerance is proposed, associated with crown root development. Results are compared with findings in Australia in acidic soils, and they highlight major opportunities for wheat improvement by selection for numbers of tillers when crops are waterlogged during vegetative growth

Linkage of water vapor distribution in the lower stratosphere to organized Asian summer monsoon convection

Accumulation of water vapor in the upper troposphere/lower stratosphere (UT/LS) over the Asian continent is a recognized feature during the boreal summer monsoon. While there has been a debate on the role of monsoon convective intensities on the UT/LS water vapor accumulations, there are ambiguities with regard to the effects of organized monsoon convection on the spatial distribution of water vapor. We provide insights into this aspect using high precision balloon measurements of water vapor from a high-elevation site Nainital (29.4° N, 79.5° E), India, located in the Himalayan foothills and satellite retrievals of water vapor from the Microwave Limb Sounder (MLS). We also use precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM) satellite (i.e., merged product 3B42 and precipitation radar 3A25 estimates of rain rate and rain type viz convective/stratiform), reanalysis circulation data, as well as numerical model simulations. We first evaluate the MLS estimates of water vapor mixing ratios with in situ high precision hygrometer balloon observations over Nainital. It is seen from our analyses of the MLS data that the LS water vapor distribution is closely linked to the organization of the South Asian monsoon convection and its influence on the UT/LS circulation. This link between LS water vapor distribution and organized monsoon convection is also captured in the in situ observations on 3 August 2016. It is evidenced that periods of organized summer monsoon convective activity over the Indian subcontinent and Bay of Bengal promote divergence of water vapor flux in the UT/LS; additionally the Tibetan anticyclonic circulation causes widespread distribution of the UT/LS water vapor. In addition to the effects of Asian monsoon convection, we also note that global climate drivers such as El Niño-Southern Oscillation (ENSO), Brewer–Dobson circulation (BDC), and Quasi-Biennial Oscillation (QBO) can contribute to nearly 38% of the UT/LS water vapor variability over the Asian monsoon region. The main result of our study indicates that widespread spatial distribution and accumulation of water vapor in the LS (about 80% of total accumulation between May and August months) tend to co-occur with organized monsoon convection, intensified divergence of water vapor flux in the UT/LS and intensified Tibetan anticyclone. On the other hand, the circulation response and LS water vapor distribution to pre-monsoon localized deep convection tend to have a limited spatial scale confined to Southeast Asia. Results from model experiments suggest that the UT/LS circulation pattern to organized monsoon convection has resemblance to stationary Rossby waves forced by organized latent heating, with the westward extending response larger by about 15° longitudes as compared to that of the pre-monsoon localized deep convection