Browsing by Author "Akhilesh Gupta"

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Association between climate and infectious diseases among children in Varanasi city, India: A prospective cohort study
(Elsevier B.V., 2021) Nidhi Singh; R.K. Mall; T. Banerjee; Akhilesh Gupta
The effects of climate on infectious diseases could influence the health impacts, particularly in children in countries with the unfair socioeconomic conditions. In a prospective cohort of 461 children under 16-years-of-age in Varanasi city, India, the association of maximum-temperature (Tmax), relative humidity (RH), absolute humidity (AH), rainfall (RF), wind-speed (WS), and solar radiation (SLR) with prevalent infectious diseases (Diarrhea, Common cold and flu, Pneumonia, Skin-disease and Malaria, and Dengue) was examined using binomial-regression, adjusting for confounders and effect modifiers (socioeconomic-status; SES and child anthropometry), from January 2017 to January 2020. Attributable-fraction (AFx) was calculated due to each climate variable for each infectious disease. The result showed that each unit (1 °C) rise in Tmax was associated with an increase in diarrhea and skin-disease cases by 3.97% (95% CI: 2.92, 5.02) and 3.94% (95% CI: 1.67, 6.22), respectively, whereas, a unit decline in Tmax was associated with an increase in cold and flu cases by 3.87% (95% CI: 2.97, 4.76). Rise in humidity (RH) was associated with increase in cases of cold and flu by 0.73% (95% CI: 0.38, 1.08) and malaria (AH) by 7.19% (95% CI: 1.51, 12.87) while each unit (1 g/m3) decrease in humidity (AH) observed increase in pneumonia cases by 3.02% (95% CI: 0.75, 5.3). WS was positively associated with diarrhea (14.16%; 95% CI: 6.52, 21.80) and negatively with dengue (17.40%; 12.32, 22.48) cases for each unit change (kmph). RF showed marginal association while SLR showed no association at all. The combined AFx due to climatic factors ranged from 9 to 18%. SES and anthropometric parameters modified the climate-morbidity association in children with a high proportion of children found suffering from stunting, wasting, and underweight conditions. Findings from this study draw the attention of government and policymakers to prioritize effective measures for child health as the present association may increase disease burden in the future under climate-change scenarios in already malnourished paediatric population through multiple pathways. © 2021 Elsevier B.V.
Climate-Smart Agriculture: An Integrated Approach to Address Climate Change and Food Security
(CRC Press, 2023) Prasanta Kumar Majhi; Ipsita Samal; Tanmaya Kumar Bhoi; Prachi Pattnaik; Chandini Pradhan; Akhilesh Gupta; Deepak Kumar Mahanta; Subrat Kumar Senapati
Climate change is undoubtedly one of the most complicated social and environmental problems the world is facing right now. This is especially true in developing countries, where climate change has been linked to things like unsustainable land management, land degradation, and greenhouse gas (GHG) fluxes in terrestrial ecosystems. All of these things lead to less agricultural production, which puts food security at risk. An increase in GHGs, largely attributable to human activity, has altered earth’s climate during the past few millennia. Similarly, increasing production costs have negatively impacted crop and livestock productivity due to climate change. This is evident in more erratic and inconsistent rainfall patterns, major floods, frequent droughts, increased pests and disease rates, and inconsistent agricultural planting seasons. Decreased agricultural output due to a failure to adjust to shifting climate conditions has severe effects on both food security and economic expansion. Increased adaptability to climate change, mitigation of climate change, and global food security through new policies, practises, and funding are the three issue areas that climate-smart agriculture (CSA) addresses simultaneously to aid agricultural systems around the world. Adopting CSA methods is crucial for economic development, food security, environmental sustainability, and ecosystem protection in developing nations because it helps the farming community mitigate the effects of climate change on agriculture. Though many resources have been poured towards improving CSA in developing nations, it has not led to widespread adoption of CSA principles on farms there. Nevertheless, CSA is highly recommended as a key component to the expansion of the agricultural sector because of the crucial function it serves. Maintaining agricultural output in the face of climate change and reducing GHG emissions necessitates the adoption of CSA on the field level. © 2024 selection and editorial matter, Habib Ali, Youming Hou, and Muhammad Bilal Tahir; individual chapters, the contributors.
Erratum: Impact of climate change on Indian agriculture: A review (Climatic Change (2006) 78, (445-478) DOI: 10.1007/s10584-005-9042-x)
(2007) R.K. Mall; Ranjeet Singh; Akhilesh Gupta; G. Srinivasan; L.S. Rathore
[No abstract available]
Evaluating the performance of RegCM4.0 climate model for climate change impact assessment on wheat and rice crop in diverse agro-climatic zones of Uttar Pradesh, India
(Springer Netherlands, 2018) R.K. Mall; Nidhi Singh; K.K. Singh; Geetika Sonkar; Akhilesh Gupta
The paper aims to explore the biasness in the RegCM climate model outputs for diverse agro-climatic zones of Uttar Pradesh, India, with emphasis on wheat (Rabi growing season) and rice (Kharif growing season) yields with and without bias correction using quantile mapping approach for the baseline period of 1971–2000. The result shows that RCM highly underestimated the maximum and minimum temperature. There exists a bias towards lower precipitation in annual and Kharif and higher in Rabi along with strikingly low intense warm (maximum temperature > 45 °C and 40 °C) and high cold events (maximum temperature < 20 °C and minimum temperature < 5 °C) in the RCM simulation and biased towards low extreme rainfall > 50 mm/day. Bias correction through quantile mapping approach, however, showed excellent agreement for annual and seasonal maximum and minimum temperature and satisfactory for extreme temperatures but drastically failed to correct rainfall. The study also quantified the biasness in the simulated potential, irrigated, and rainfed wheat and rice yield using DSSAT (Decision Support System for Agro-technology Transfer) crop model by employing observed, RCM baseline, and RCM baseline bias-corrected weather data. The grain yields of RCM-simulated wheat and rice were high while the bias-corrected yield has shown good agreement with corresponding observed yield. Future research must account for the development of more reliable RCM models and explicitly bias correction method in specific to complement future analysis. © 2018, Springer Nature B.V.
Evidence of asymmetric change in diurnal temperature range in recent decades over different agro-climatic zones of India
(John Wiley and Sons Ltd, 2021) Rajesh Kumar Mall; Manisha Chaturvedi; Nidhi Singh; Rajeev Bhatla; Ravi Shankar Singh; Akhilesh Gupta; Dev Niyogi
Diurnal temperature range (DTR) is an important indicator of climatic change and a critical thermal metric to assess the impact on agriculture and human health. This study investigates the seasonal, annual and decadal changes in the spatio-temporal trend in DTR and air temperatures (maximum: Tmax and minimum: Tmin) during 1951–2016 and solar radiation (Srad) during 1984–2016 over 14 different agro-climatic zones (ACZs) in India. The changes in the DTR trend between two time periods:1951–2016 and 1991–2016 (recent period) are also assessed. The results indicate an overall increasing trend in DTR (0.038°C/decade), Tmax (0.078°C/decade, significant), Tmin (0.049°C/decade) during 1951–2016 and Srad (0.10 MJ/m2/day/decade) during 1984–2016. However, a decreasing trend in DTR (−0.02°C/decade) and a significant increasing trend in Tmin (0.210°C/decade) was noted during 1991–2016. The decadal changes showed an evident decline in DTR during the recent period since 1991. The relative increase in Tmin (0.21°C/decade, significant) compared to Tmax (0.18°C/decade) resulted in a decreasing DTR trend. This was evident across the 5 out of the 14 agro-climatic zones for the 1991–2016 period. The seasonal analysis showed a significant (95%) increasing trend in DTR during pre-monsoon and monsoon (1951–2016), and a negative trend for the post-monsoon and monsoon since 1991. There were also interesting spatial differences found with the ACZs in the north-west, parts of Gangetic plain, north-east, and central India exhibiting negative DTR trends. The effect of Srad is larger on Tmax than Tmin; therefore, the decrease in Srad in parts of Gangetic plain likely contributed to a smaller increase in Tmax relative to Tmin and led to a decreasing trend in DTR. At the same time, the west coast, east coast, and southern region show positive trends. The observational analysis finds a distinct increase in the Tmin and also highlights the need for future assessments to continue investigate the causes of these spatio-temporal changes found in this study. © 2020 Royal Meteorological Society
Impact of climate change on Indian agriculture: A review
(Springer Netherlands, 2006) R.K. Mall; Ranjeet Singh; Akhilesh Gupta; G. Srinivasan; L.S. Rathore
During the recent decade, with the growing recognition of the possibility of climate change and clear evidence of observed changes in climate during 20th century, an increasing emphasis on food security and its regional impacts has come to forefront of the scientific community. In recent times, the crop simulation models have been used extensively to study the impact of climate change on agricultural production and food security. The output provided by the simulation models can be used to make appropriate crop management decisions and to provide farmers and others with alternative options for their farming system. It is expected that in the coming decades with the increased use of computers, the use of simulation models by farmers and professionals as well as policy and decision makers will increase. In India, substantial work has been done in last decade aimed at understanding the nature and magnitude of change in yield of different crops due to projected climate change. This paper presents an overview of the state of the knowledge of possible effect of the climate variability and change on food grain production in India. © 2006 Springer Science+Business Media, Inc.
Impact of climate variability on human health: A pilot study in tertiary care hospital of eastern Uttar Pradesh, India
(India Meteorological Department, 2017) R.K. Mall; Nidhi Singh; R. Prasad; A. Tompkins; Akhilesh Gupta
This study is an attempt to find out the effect of climate variables on respiratory, cardiovascular, vector-borne and diarrheal diseases from 2004-2013 carried out at Sir Sunder Lal hospital, Varanasi, Uttar Pradesh with focus on eastern Uttar Pradesh. The study shows that cases of Chronic Obstructive Pulmonary Disorder (COPD) and Cardiovascular Disorders (CVD) didn’t show any significant relation with any of the climate variables. With increase of 1 °C mean maximum monthly temperature the estimated decrease in number of Tuberculosis (TB) patients was 4 (95% CI = 4.95-3.05) while a 1°C increase in minimum monthly temperature showed increase of TB patients by 4 (95% CI = 4.95-3.05). One percent increase of monthly averaged relative humidity is estimated to increase the one pneumonia patients (95% CI = 1.95-0.05) at any given month. One-degree increase in given monthly temperature will increase the load of one diarrhea patients (95% CI = 1.95-0.05) monthly. Dengue and Malaria patients showed increasing monthly malaria cases by 5 (95% CI = 5.95-4.05) with 1˚C rise in minimum monthly temperature and by 1 patient (95% CI = 1.95-0.05) with increase in 1% relative humidity. Encephalitis showed an increase of one patient load (95% CI = 1.95-0.05) with monthly increase of 1°C in maximum temperature. The study shows advance knowledge of health information, on timescales of seasons to decades ahead, would aid effective planning of health response measures and infrastructure at local and regional scale. © 2017, India Meteorological Department. All rights reserved.
Indian sugarcane under warming climate: A simulation study
(Elsevier B.V., 2023) Rohit Jaiswal; R.K. Mall; Shubhi Patel; Nidhi Singh; Nisha Mendiratta; Akhilesh Gupta
Multi-model climate projections are increasingly used to quantify the impacts of climate change on major staple crops under different climate change scenarios. Despite uncertainty associated with different climate projections, it helps in providing a direction and magnitude of change in crop production in future with different uncertainty levels. In this study, we used the CANEGRO-Sugarcane crop model driven by downscaled and bias-corrected simulations forced by different regional climate models (RCMs) for the mid-future (2040–2069) and far-future (2070–2099) under the two emission scenarios RCP4.5 and RCP8.5 to simulate the effect of climate change on sugarcane's stalk fresh mass (SFM) and Sucrose Mass (SM) over major sugarcane growing states of India. The result showed, out of three phenological phases analyzed, two were found to be Shortened (planting to emergence up to 14.5 days and emergence to stalk elongation up to 6.3 days) and one i.e., peak population to harvest get extended up to 9.5 days under RCP8.5, far-future. An increase in SFM is projected substantially in the mid-future under RCP 8.5 for the tropical state of Gujarat (11.2–18.1 %) and the least for Odisha (6.8 % to 10.7). On the contrary, SM was found to decrease overall except for the states of Uttar Pradesh, Maharashtra, Gujrat, and Andhra Pradesh. The changes in the SFM and SM were found to be regulated by the increase in maximum (Tmax) and minimum temperature (Tmin), decline in solar radiation (Srad), leading to an increase in SFM and a reduction in sugar content. Therefore, decline in SM in the future which may cause economic loss as sugarcane is one of the most important cash crops of India. With uncertainties in the magnitude of change, the findings are useful for plant breeders and policymakers to develop appropriate strategies to minimize the loss and enhance sugar production. © 2023
Integrated assessment of extreme events and hydrological responses of Indo-Nepal Gandak River Basin
(Springer Science and Business Media B.V., 2021) Pawan K. Chaubey; Prashant K. Srivastava; Akhilesh Gupta; R.K. Mall
Changes in climate cause significant alterations in morphometric parameters and may lead to hydro-meteorological hazards. In this study, an attempt has been made to identify drainage morphometric characteristics through topographic, geologic and hydrological information to assess the extreme weather events (flood) over the Gandak River Basin (GRB). The standardized precipitation index (SPI) and rainfall anomaly index (RAI) were used for deducing extreme rainfall incidences derived from the Tropical Rainfall Measuring Mission precipitation datasets. An assembled frequency distribution as well as trends in RAI and SPI was calculated to understand the hydro-climatological behaviour of the basin. During the monsoon season, the years 1998, 2007, 2011, 2013 and 2017 witnessed the extreme flood events. The variations in heavy and intense rainfall in short time can be linked to extreme flood events, which leads to channel shifting and modifications, can be deduced from provided asymmetric factors and sinuosity index. The results illustrated that both the monsoonal rainfall and the frequency of extreme rainfall over the basin are increasing, which could be a reason for a high severity and frequency of flood events in the GRB. © 2020, Springer Nature B.V.
Performance assessment of evapotranspiration estimated from different data sources over agricultural landscape in Northern India
(Springer, 2020) Prashant K. Srivastava; Prachi Singh; R.K. Mall; Rajani K. Pradhan; Michaela Bray; Akhilesh Gupta
Accurate estimation of evapotranspiration is generally constrained due to lack of required hydrometeorological datasets. This study addresses the performance analysis of reference evapotranspiration (ETo) estimated from NASA/POWER, National Center for Environmental Prediction (NCEP) global reanalysis data before and after dynamical downscaling through the Weather Research and Forecasting (WRF) model. The state-of-the-art Hamon’s and Penman-Monteith’s methods were utilized for the ETo estimation in the Northern India. The performance indices such as bias, root mean square error (RMSE), and correlation (r) were calculated, which showed the values 0.242, 0.422, and 0.959 for NCEP data (without downscaling) and 0.230, 0.402, and 0.969 for the downscaled data respectively. The results indicated that after WRF downscaling, there was some marginal improvement found in the ETo as compared to the without downscaling datasets. However, a better performance was found in the case of NASA/POWER datasets with bias, RMSE, and correlation values of 0.154, 0.348, and 0.960 respectively. In overall, the results indicated that the NASA/POWER and WRF downscaled data can be used for ETo estimation, especially in the ungauged areas. However, NASA/POWER is recommended as the ETo calculations are less computationally expensive and easily available than performing WRF simulations. © 2020, Springer-Verlag GmbH Austria, part of Springer Nature.
Rainfall variability analysis of Uttar Pradesh for crop planning and management
(India Meteorological Department, 2018) Arvind Kumar; P. Tripathi; Akhilesh Gupta; K.K. Singh; P.K. Singh; Ranjit Singh; R.S. Singh; Amitabh Tripathi
Time series analysis and statistical significance of trends in rainfall data was carried out using standard Mann-Kendall test statistics. The non-parametric Mann-Kendall (M-K) statistical rank test, which is widely used in climate research, was employed in this study to find out fluctuations and presence of trend in time series data of rainfall at a single station, as well as regional averages. Analysis of rainfall data (1981-2012) of Uttar Pradesh reveals significant decreasing trend in total quantum of annual rainfall. It was also noticed that the frequency of occurrence of annual rainfall below normal was less before 1990s while increased after 1990s. The amount of annual rainfall decreased significantly after 1996 from 1040.5 mm to 988 mm, i.e., a decrease of 5 percent. A very interesting trend has been noticed for the quantum of monthly rainfall, i.e., it was found significantly decreased after 1996 for the months of winter season (October-February) while at par for the months of summer season except May only which was found increased as compared to before 1996. The quantum of monthly rainfall was found significantly decreased after 1996 for initial and last months of monsoon season, i.e., June and September while increased for the middle months, i.e., July and August as compared to before 1996. The study of decadal variability in annual rainfall showed that an alternate decreasing and increasing trend in all the three decades viz., 1981-1990, 1991-2000 and 2001-2010. © 2018, India Meteorological Department. All rights reserved.
Rice (Oryza sativa L.) yield gap using the CERES-rice model of climate variability for different agroclimatic zones of India
(Indian Academy of Sciences, 2016) P.K. Singh; K.K. Singh; L.S. Rathore; A.K. Baxla; S.C. Bhan; Akhilesh Gupta; G.B. Gohain; R. Balasubramanian; R.S. Singh; R.K. Mall
The CERES (Crop Estimation through Resource and Environment Synthesis)-rice model incorporated in DSSAT version 4.5 was calibrated for genetic coefficients of rice cultivars by conducting field experiments during the kharif season at Jorhat, Kalyani, Ranchi and Bhagalpur, the results of which were used to estimate the gap in rice yield. The trend of potential yield was found to be positive and with a rate of change of 26, 36.9, 57.6 and 3.7 kg ha-1 year-1 at Jorhat, Kalyani, Ranchi and Bhagalpur districts respectively. Delayed sowing in these districts resulted in a decrease in rice yield to the tune of 35.3, 1.9, 48.6 and 17.1 kg ha-1 day-1 respectively. Finding reveals that DSSAT crop simulation model is an effective tool for decision support system. Estimation of yield gap based on the past crop data and subsequent adjustment of appropriate sowing window may help to obtain the potential yields.
Simulating the Impacts of Climate Change on Sugarcane in Diverse Agro-climatic Zones of Northern India Using CANEGRO-Sugarcane Model
(Springer, 2020) Geetika Sonkar; Nidhi Singh; R.K. Mall; K.K. Singh; Akhilesh Gupta
CANEGRO-Sugarcane model was used to assess the impact of climate change on sugarcane in different combinations of elevated temperature and CO2 concentrations. Additionally, we used dynamically downscaled bias-corrected regional climate model (RCM) data using RegCM4 under RCP4.5 scenario (2040–2060) to project the future change in sugarcane stalk fresh mass (SFM) and sucrose mass (SM). The results showed an increase in temperature, rainfall and solar radiation in the future projections at the study site. The SFM and SM were found to be vulnerable (3–25% decrease) by increasing temperature (1–4 °C), however, a higher concentration (2–14% increase) was observed for both SFM and SM under elevated CO2 levels (450–850 ppm). The combined effect of increased temperature and elevated CO2 had a beneficial effect on SFM but negative on SM (more for rainfed condition). Overall, SFM was projected to increase by 3–39% (rainfed) and 7–47% (irrigated) in 2040–2060 relative to 1971–2000 in diverse agro-climatic zones of the region. Similarly, SM was projected to decrease by 9–69% (rainfed) and 6–37% (irrigated). In general, water stress conditions combined with the projected increase in temperature adversely affected the sugarcane. The findings suggest the development of a efficient water use, heat-tolerant cane variety and improved farm management strategies in the near future to assist the sugar industry and to adapt to the changing climate in northern India. This is required in the greater perspective of decrease in sucrose mass in spite of double-fold increase in CO2. © 2019, Society for Sugar Research & Promotion.
System modeling and signal processing of microwave Doppler radar for cardiopulmonary sensing
(Institute of Electrical and Electronics Engineers Inc., 2015) Manoj Kumar Singh; Akhilesh Gupta; Salony Permanand; Umesh Singh; O.P. Singh; Yong Hoon Kim; Ashish Kumar Singh
In this paper, we present the modeling, simulation and signal processing of Doppler radar for heart beat and reparation sensing. Distance dependency of accuracy of heart and respiration signal from radar output is investigated and verified through simulation. The model is experimentally validated with commercially available motion detector DNO-341. The based band signal containing respiration and heartbeat signatures are low-pass filtered (0.7 Hz) for the respiration and band-pass filtered (0.9-2.5 Hz) for the heart signal. The estimated heart beat per minute and peak-peak interval is verified with result obtained from standard ECG measurement. © 2015 IEEE.
Water resources and climate change: An Indian perspective
(2006) R.K. Mall; Akhilesh Gupta; Ranjeet Singh; R.S. Singh; L.S. Rathore
In recent times, several studies around the globe show that climatic change is likely to impact significantly upon freshwater resources availability. In India, demand for water has already increased manifold over the years due to urbanization, agriculture expansion, increasing population, rapid industrialization and economic development. At present, changes in cropping pattern and land-use pattern, over-exploitation of water storage and changes in irrigation and drainage are modifying the hydrological cycle in many climate regions and river basins of India. An assessment of the availability of water resources in the context of future national requirements and expected impacts of climate change and its variability is critical for relevant national and regional long-term development strategies and sustainable development. This article examines the potential for sustainable development of surface water and groundwater resources within the constraints imposed by climate change and future research needs in India.