Browsing by Author "Rajesh Kumar Mall"

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Advances in remote sensing in measuring urban heat island effect and its management
(Elsevier, 2023) Saumya Singh; Rajesh Kumar Mall; A. Chaturvedi; Nidhi Singh; Prashant K. Srivastava
The urban environment has undergone rapid change as a result of urbanization and the increase in global temperature, which has affected the energy balance and heat fluxes. The urban heat island (UHI) phenomenon is brought on by these changes in geometry, building materials, land use, and land cover, among other factors, which raise the temperature in cities relative to their rural and suburban surroundings. The microclimate, ecology, air quality, and infrastructure are all significantly impacted by the UHI effect, which also has a significant impact on energy needs. Meteorological data cannot be used to measure the UHI effect due to large spatial heterogeneity and the lack of dense weather station networks. Remote sensing offers an effective way to track, measure, and manage the UHI effect in such circumstances. Thermal remote sensing technology has made it possible to estimate the UHI effect in high spatial and temporal resolution. The effectiveness of the technique in multicity analysis has been reported in several studies, prompting research to advance the techniques. Urban ecosystems can be transformed from unsustainable to sustainable one on a global scale with the development of multispectral to hyperspectral imaging. © 2024 Elsevier Ltd. All rights reserved.
Aerosol characteristics in CMIP6 models' global simulations and their evaluation with the satellite measurements
(John Wiley and Sons Ltd, 2024) Jaisankar Bharath; Tumuluru Venkata Lakshmi Kumar; Vanda Salgueiro; Maria João Costa; Rajesh Kumar Mall
Global and regional trends of the Aerosol Optical Depth (AOD) from Coupled Model Intercomparison Project (CMIP) Phase 6 simulations for the study period 1971–2014 were compared against the satellite retrievals and the intermodel variations were analysed. The AOD from multimodel mean (MMM) of eight general circulation models (GCMs) has been evaluated against the Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging Spectro Radiometer (MISR) AOD for the 2001–2014 period. Angstrom exponents (AE and its first derivative) that represent the size distribution of aerosols are estimated globally from the perturbed initial condition ensemble of MRI-ESM2-0 and MPI-ESM-1-2-HAM models to report the aerosol variations through their size distribution. We found that the global AOD obtained from the MMM8 showed an insignificant decreasing trend, while this trend is significantly positive over the northern tropical region. The MMM8 has overestimated the MODIS AOD over North Africa, India, China, and Australia while this overestimation is confined to North Africa and eastern China when compared against MISR AOD. The absolute percent bias of MMM8 is 28.1% and 24.1% over the globe when compared against MODIS and MISR AOD, respectively. The spatial pattern of AE showed the dominance of fine- and coarse-mode particles during the boreal/austral winter and summer seasons, respectively, that replicate the seasonality of aerosols. The AE derived from MPI-ESM-1-2-HR demonstrated better agreement with AATSR SU's (Advanced Along Track Scanning Radiometer instrument series, with the algorithm developed by Swansea University) AE (550–870 nm). On the other hand, MRI-ESM2-0 consistently underestimated AE across different regions and wavelength ranges, suggesting an over representation of larger aerosol particles in the model's portrayal of aerosol size distribution compared to satellite observations. © 2023 Royal Meteorological Society.
Ambient air pollution and daily mortality in ten cities of India: a causal modelling study
(Elsevier B.V., 2024) Jeroen de Bont; Bhargav Krishna; Massimo Stafoggia; Tirthankar Banerjee; Hem Dholakia; Amit Garg; Vijendra Ingole; Suganthi Jaganathan; Itai Kloog; Kevin Lane; Rajesh Kumar Mall; Siddhartha Mandal; Amruta Nori-Sarma; Dorairaj Prabhakaran; Ajit Rajiva; Abhiyant Suresh Tiwari; Yaguang Wei; Gregory A Wellenius; Joel Schwartz; Poornima Prabhakaran; Petter Ljungman
Background: The evidence for acute effects of air pollution on mortality in India is scarce, despite the extreme concentrations of air pollution observed. This is the first multi-city study in India that examines the association between short-term exposure to PM2·5 and daily mortality using causal methods that highlight the importance of locally generated air pollution. Methods: We applied a time-series analysis to ten cities in India between 2008 and 2019. We assessed city-wide daily PM2·5 concentrations using a novel hybrid nationwide spatiotemporal model and estimated city-specific effects of PM2·5 using a generalised additive Poisson regression model. City-specific results were then meta-analysed. We applied an instrumental variable causal approach (including planetary boundary layer height, wind speed, and atmospheric pressure) to evaluate the causal effect of locally generated air pollution on mortality. We obtained an integrated exposure–response curve through a multivariate meta-regression of the city-specific exposure–response curve and calculated the fraction of deaths attributable to air pollution concentrations exceeding the current WHO 24 h ambient PM2·5 guideline of 15 μg/m3. To explore the shape of the exposure–response curve at lower exposures, we further limited the analyses to days with concentrations lower than the current Indian standard (60 μg/m3). Findings: We observed that a 10 μg/m3 increase in 2-day moving average of PM2·5 was associated with 1·4% (95% CI 0·7–2·2) higher daily mortality. In our causal instrumental variable analyses representing the effect of locally generated air pollution, we observed a stronger association with daily mortality (3·6% [2·1–5·0]) than our overall estimate. Our integrated exposure–response curve suggested steeper slopes at lower levels of exposure and an attenuation of the slope at high exposure levels. We observed two times higher risk of death per 10 μg/m3 increase when restricting our analyses to observations below the Indian air quality standard (2·7% [1·7–3·6]). Using the integrated exposure–response curve, we observed that 7·2% (4·2%–10·1%) of all daily deaths were attributed to PM2·5 concentrations higher than the WHO guidelines. Interpretation: Short-term PM2·5 exposure was associated with a high risk of death in India, even at concentrations well below the current Indian PM2·5 standard. These associations were stronger for locally generated air pollutants quantified through causal modelling methods than conventional time-series analysis, further supporting a plausible causal link. Funding: Swedish Research Council for Sustainable Development. © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
Appraisal of hydro-meteorological factors during extreme precipitation event: case study of Kedarnath cloudburst, Uttarakhand, India
(Springer, 2020) Shailendra Pratap; Prashant K. Srivastava; Ashish Routray; Tanvir Islam; Rajesh Kumar Mall
Flash flood is an uncertain and most catastrophic disaster worldwide that causes socio-economic problems, devastation and loss of infrastructure. One of the major triggering factors of flash floods is the extreme events like cloudburst that causes flooding of area within a short span of time. Therefore, this study aims to understand the variations in hydro-meteorological variables during the devastating Kedarnath cloudburst in the Uttarakhand, India. The hydro-meteorological variables were collected from the global satellites such as Moderate Resolution Imaging Spectroradiometer, Tropical Rainfall Measuring Mission, modelled datasets from Decision Support System for Agrotechnology Transfer and National Center for Environmental Prediction (NCEP). For the validation of satellite meteorological data, the NCEP Global analysis data were downscaled using Weather Research and Forecasting model over the study area to achieve the meteorological variables’ information. The meteorological factors such as atmospheric pressure, atmospheric temperature, rainfall, cloud water content, cloud fraction, cloud particle radius, cloud mixing ratio, total cloud cover, wind speed, wind direction and relative humidity were studied during the cloudburst, before as well as after the event. The outcomes of this study indicate that the variability in hydro-meteorological variables over the Kedarnath had played a significant role in triggering the cloudburst in the area. The results showed that during the cloudburst, the relative humidity was at the maximum level, the temperature was very low, the wind speed was slow and the total cloud cover was found at the maximum level. It is expected that because of this situation a high amount of clouds may get condensed at a very rapid rate and resulted in a cloudburst over the Kedarnath region. © 2020, Springer Nature B.V.
Changes in Extremes Rainfall Events in Present and Future Climate Scenarios over the Teesta River Basin, India
(MDPI, 2023) Pawan Kumar Chaubey; Rajesh Kumar Mall; Prashant K. Srivastava
Globally, changes in hydroclimate extremes such as extreme precipitation events influence water resources, natural environments, and human health and safety. During recent decades, India has observed an enormous increase in rainfall extremes during the summer monsoon (June to September) seasons. However, future extreme rainfall events have significant uncertainty at the regional scale. Consequently, a comprehensive study is needed to evaluate the extreme rainfall events at a regional river basin level in order to understand the geomorphological characteristics and pattern of rainfall events. In the above purview, the current research focuses on changes in extreme rainfall events obtained through observed gridded datasets and future scenarios of climate models derived through the Coupled Model Intercomparison Project (CMIP). The results highlight a significant rise in the extremes of precipitation events during the first half of the 21st century. In addition, our study concludes that accumulated precipitation will increase by five days in the future, while the precipitation maxima will increase from 200 to 300 mm/day at the 2-year, 50-year, and 100-year return periods. Finally, it is found that during the middle of the 21st century the 23.37% number of events will increase over the TRB at the 90th percentile. © 2023 by the authors.
Double transplantation as a climate resilient and sustainable resource management strategy for rice production in eastern Uttar Pradesh, north India
(Academic Press, 2023) Pradeep Kumar Dubey; Rajan Chaurasia; Krishna Kumar Pandey; Amit Kumar Bundela; Ajeet Singh; Gopal Shankar Singh; Rajesh Kumar Mall; Purushothaman Chirakkuzhyil Abhilash
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Drought identification and trend analysis using long-term chirps satellite precipitation product in bundelkhand, india
(MDPI AG, 2021) Varsha Pandey; Prashant K. Srivastava; Sudhir K. Singh; George P. Petropoulos; Rajesh Kumar Mall
Drought hazard mapping and its trend analysis has become indispensable due to the aggravated impact of drought in the era of climate change. Sparse observational networks with minimal maintenance limit the spatio-temporal coverage of precipitation data, which has been a major constraint in the effective drought monitoring. In this study, high-resolution satellite-derived Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data has been used for computation of Standardized Precipitation Index (SPI). The study was carried out in Bundelkhand region of Uttar Pradesh, India, known for its substantial drought occurrences with poor drought management plans and lack of effective preparedness. Very limited studies have been carried out in assessing the spatio-temporal drought in this region. This study aims to identify district-wide drought and its trend characterization from 1981 to 2018. The run theory was applied for quantitative drought assessment; whereas, the Mann-Kendall (MK) test was performed for trend analysis at seasonal and annual time steps. Results indicated an average of nine severe drought events in all the districts in the last 38 years, and the most intense drought was recorded for the Jalaun district (1983–1985). A significant decreasing trend is observed for the SPI1 (at 95% confidence level) during the post-monsoon season, with the magnitude varying from −0.16 to −0.33 mm/month. This indicates the increasing severity of meteorological drought in the area. Moreover, a non-significant falling trend for short-term drought (SPI1 and SPI3) annually and short-and medium-term drought (SPI1, SPI3, and SPI6) in winter months have been also observed for all the districts. The output of the current study would be utilized in better understanding of the drought condition through elaborate trend analysis of the SPI pattern and thus helps the policy makers to devise a drought management plan to handle the water crisis, food security, and in turn the betterment of the inhabitants. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Earth Observation in Urban Monitoring: Techniques and Challenges
(Elsevier, 2023) Amit Kumar; Prashant K. Srivastava; Rajesh Kumar Mall
Earth Observation in Urban Monitoring: Techniques and Challenges presents the latest techniques of remote sensing in urban monitoring, along with methods for quantitative and qualitative assessment using state-of-the-art Earth observation technologies. The book details the advances of remote sensing technologies in urban environmental monitoring for a range of practical and research applications, Earth observation datasets, remote sensing of environmental considerations, geostatistical techniques and resilience perspectives. Chapters cover sensor applications, urban growth modelling, SAR applications, surveying techniques, satellite time series analysis and a variety of other remote sensing technologies for urban monitoring. Each chapter includes detailed case studies at a variety of scales and from a variety of geographies, offering up-to-date, global, urban monitoring methodologies for researchers, scientists and academics in remote sensing, geospatial research, environmental science and sustainability. © 2024 Elsevier Ltd. All rights reserved.
Effect of Lockdown Amid COVID-19 on Ambient Air Quality in 16 Indian Cities
(Frontiers Media S.A., 2021) Amit Kumar Mishra; Prashant Rajput; Amit Singh; Chander Kumar Singh; Rajesh Kumar Mall
The COVID-19 pandemic has affected severely the economic structure and health care system, among others, of India and the rest of the world. The magnitude of its aftermath is exceptionally devastating in India, with the first case reported in January 2020, and the number has risen to ~31.3 million as of July 23, 2021. India imposed a complete lockdown on March 25, which severely impacted migrant population, industrial sector, tourism industry, and overall economic growth. Herein, the impacts of lockdown and unlock phases on ambient atmospheric air quality variables have been assessed across 16 major cities of India covering the north-to-south stretch of the country. In general, all assessed air pollutants showed a substantial decrease in AQI values during the lockdown compared with the reference period (2017–2019) for almost all the reported cities across India. On an average, about 30–50% reduction in AQI has been observed for PM2.5, PM10, and CO, and maximum reduction of 40–60% of NO2 has been observed herein, while the data was average for northern, western, and southern India. SO2 and O3 showed an increase over a few cities as well as a decrease over the other cities. Maximum reduction (49%) in PM2.5 was observed over north India during the lockdown period. Furthermore, the changes in pollution levels showed a significant reduction in the first three phases of lockdown and a steady increase during subsequent phase of lockdown and unlock period. Our results show the substantial effect of lockdown on reduction in atmospheric loading of key anthropogenic pollutants due to less-to-no impact from industrial activities and vehicular emissions, and relatively clean transport of air masses from the upwind region. These results indicate that by adopting cleaner fuel technology and avoiding poor combustion activities across the urban agglomerations in India could bring down ambient levels of air pollution at least by 30%. Copyright © 2021 Mishra, Rajput, Singh, Singh and Mall.
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
Excess Mortality Risk Due to Heat Stress in Different Climatic Zones of India
(American Chemical Society, 2024) Rohit Kumar Choudhary; Pallavi Joshi; Santu Ghosh; Dilip Ganguly; Kalpana Balakrishnan; Nidhi Singh; Rajesh Kumar Mall; Alok Kumar; Sagnik Dey
India is at a high risk of heat stress-induced health impacts and economic losses owing to its tropical climate, high population density, and inadequate adaptive planning. The health impacts of heat stress across climate zones in India have not been adequately explored. Here, we examine and report the vulnerability to heat stress in India using 42 years (1979-2020) of meteorological data from ERA-5 and developed climate-zone-specific percentile-based human comfort class thresholds. We found that the heat stress is usually 1-4 °C higher on heatwave (HW) days than on nonheatwave (NHW) days. However, the stress on NHW days remains considerable and cannot be neglected. We then showed the association of a newly formulated India heat index (IHI) with daily all-cause mortality in three cities - Delhi (semiarid), Varanasi (humid subtropical), and Chennai (tropical wet and dry), using a semiparametric quasi-Poisson regression model, adjusted for nonlinear confounding effects of time and PM2.5. The all-cause mortality risk was enhanced by 8.1% (95% confidence interval, CI: 6.0-10.3), 5.9% (4.6-7.2), and 8.0% (1.7-14.2) during “sweltering” days in Varanasi, Delhi, and Chennai, respectively, relative to “comfortable” days. Across four age groups, the impact was more severe in Varanasi (ranging from a 3.2 to 7.5% increase in mortality risk for a unit rise in IHI) than in Delhi (2.6-4.2% higher risk) and Chennai (0.9-5.7% higher risk). We observed a 3-6 days lag effect of heat stress on mortality in these cities. Our results reveal heterogeneity in heat stress impact across diverse climate zones in India and call for developing an early warning system keeping in mind these regional variations. © 2023 American Chemical Society
Long-term trend analysis of precipitation and extreme events over kosi river basin in india
(MDPI AG, 2021) Prashant K. Srivastava; Rajani Kumar Pradhan; George P. Petropoulos; Varsha Pandey; Manika Gupta; Aradhana Yaduvanshi; Wan Zurina Wan Jaafar; Rajesh Kumar Mall; Atul Kumar Sahai
Analysis of spatial and temporal changes of long-term precipitation and extreme precipitation distribution at a local scale is very important for the prevention and mitigation of water-related disasters. In the present study, we have analyzed the long-term trend of 116 years (1901-2016) of precipitation and distribution of extreme precipitation index over the Kosi River Basin (KRB), which is one of the frequent flooding rivers of India, using the 0.25° × 0.25° resolution gridded precipitation datasets obtained from the Indian Meteorological Department (IMD), India. The non-parametric Mann-Kendall trend test together with Sen’s slope estimator was employed to determine the trend and the magnitude of the trend of the precipitation time series. The annual and monsoon seasons revealed decreasing trends with Sen’s slope values of −1.88 and −0.408, respectively. For the extreme indices viz. R10 and R20 days, a decreasing trend from the northeastern to the southwest part of the basin can be observed, whereas, in the case of highest one-day precipitation (RX1 day), no clear trend was found. The information provided through this study can be useful for policymakers and may play an important role in flood management, runoff, and understanding related to the hydrological process of the basin. This will contribute to a better understanding of the potential risk of changing rainfall patterns, especially the extreme rainfall events due to climatic variations. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Modeling Long-term Groundwater Levels By Exploring Deep Bidirectional Long Short-Term Memory using Hydro-climatic Data
(Springer Science and Business Media B.V., 2021) Sangita Dey; Arabin Kumar Dey; Rajesh Kumar Mall
Inevitable issues concerning the sustainability of groundwater resources are crucial under the present climatic situation. Therefore, the prevision of groundwater environments may able to reinforce the management system. In this respect present study considered a new method to predict long-term groundwater level framework as an alternative option of expensive physical models. The proposed Bidirectional Long Short-Term Memory (BLSTM) model can efficiently capture Spatio-temporal features from historical data. A highway LSTM network is also introduced within the architecture of the model to optimize the analysis. The relative performance of the proposed BLSTM with the highway LSTM (BHLSTM) network compared with simple BLSTM. Stack size increment of the BHLSTM and BLSTM layers can enhance the learning ability and improve by incorporating straight LSTM at the top of the architecture. The proposed model was applied to predict the groundwater level exemplary of the Varuna River basin for twenty years. The model incorporates the historical annual average of total precipitation, temperature, relative humidity, actual evapotranspiration, and groundwater level data to develop and validate the models. The result shows that the signals are captured reasonably well by a stack of four BHLSTM and straight LSTM models in forecasting groundwater levels. The predicted water level range (0—20 mbgl) has four categories low, medium, high, and very high which eventually, illustrates the water-threatened situation in upcoming years in the study area. It is also recommended to exploring this proposed method for further improvements and extensions towards interpreting spatial features. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.
Potential impact of rainfall variability on groundwater resources: a case study in Uttar Pradesh, India
(Springer, 2020) Sangita Dey; Diva Bhatt; Saidul Haq; Rajesh Kumar Mall
Groundwater systems are largely influenced by rainfall variability which is considered the principal source of recharge. The present study explores the relation between the long-term rainfall (1992–2014) and the corresponding water table variation over the Varanasi district. The temporal trends of both the water table and long-term rainfall were analyzed using non-parametric Mann-Kendall time-series trend test. The district experienced an annual rainfall average of 876 mm during the study period. In the recent decade (2003–2014), the amount of annual rainfall and rainy days declined by 42 mm and 8 days, respectively, were compared with previous decade (1992–2002). The water table fluctuation had also shown decreasing trend in the recent decade and were compared with the previous decade. The frequent fluctuations in rainfall anomaly and water table fluctuation had been related to El Nino and La Nina events to study the impact of these events at regional scale. The intense cultivation of water intensive crops as well as rainfall variation was found to be one of the major causes behind the water table fluctuation in the study area. Therefore, artificial water recharge and change in cropping pattern through cultivating less water consuming crops with efficient irrigation technologies of water management may help to overcome the upcoming adverse situations. © 2020, Saudi Society for Geosciences.
Reference evapotranspiration retrievals from a mesoscale model based weather variables for soil moisture deficit estimation
(MDPI, 2017) Prashant K. Srivastava; Dawei Han; Aradhana Yaduvanshi; George P. Petropoulos; Sudhir Kumar Singh; Rajesh Kumar Mall; Rajendra Prasad
Reference Evapotranspiration (ETo) and soil moisture deficit (SMD) are vital for understanding the hydrological processes, particularly in the context of sustainable water use efficiency in the globe. Precise estimation of ETo and SMD are required for developing appropriate forecasting systems, in hydrological modeling and also in precision agriculture. In this study, the surface temperature downscaled from Weather Research and Forecasting (WRF) model is used to estimate ETo using the boundary conditions that are provided by the European Center for Medium Range Weather Forecast (ECMWF). In order to understand the performance, the Hamon's method is employed to estimate the ETo using the temperature from meteorological station and WRF derived variables. After estimating the ETo, a range of linear and non-linear models is utilized to retrieve SMD. The performance statistics such as RMSE, %Bias, and Nash Sutcliffe Efficiency (NSE) indicates that the exponential model (RMSE = 0.226; %Bias = -0.077; NSE = 0.616) is efficient for SMD estimation by using the Observed ETo in comparison to the other linear and non-linear models (RMSE range = 0.019-0.667; %Bias range = 2.821-6.894; NSE = 0.013-0.419) used in this study. On the other hand, in the scenario where SMD is estimated using WRF downscaled meteorological variables based ETo, the linear model is found promising (RMSE = 0.017; %Bias = 5.280; NSE = 0.448) as compared to the non-linear models (RMSE range = 0.022-0.707; %Bias range = -0.207--6.088; NSE range = 0.013-0.149). Our findings also suggest that all the models are performing better during the growing season (RMSE range = 0.024-0.025; %Bias range = -4.982--3.431; r = 0.245-0.281) than the non-growing season (RMSE range = 0.011-0.12; %Bias range = 33.073-32.701; r = 0.161-0.244) for SMD estimation. © 2017 by the authors.
Spatio-temporal variability of summer monsoon surface air temperature over India and its regions using Regional Climate Model
(John Wiley and Sons Ltd, 2021) Shruti Verma; Rajeev Bhatla; Soumik Ghosh; Palash Sinha; Rajesh Kumar Mall; Manas Pant
In this study, a dynamically downscaled regional climate model (RegCM4.3) is used to study the Indian summer monsoon (ISM) surface air temperature over the South-Asia CORDEX domain using six convection schemes during 1986–2010. The spatial and temporal variability of mean surface air temperature has been analysed with reference to the India Meteorological Department (IMD) analysis data using various statistical scores. The sensitivity experiments in selecting the best convective parameterized schemes have been performed in simulating the surface air temperature during the summer monsoon season (June–September) over India and its five sub-regions such as Northwest India, Northcentral India, West Peninsular India, Eastern Peninsular India, and Southern Peninsular India. The model results show the tendency of overestimation of surface air temperature mainly in four cumulus parameterization schemes (CPSs) that is, Tiedtke, Emanuel, Mix98, and Mix99 of RegCM4.3 during the JJAS, where Grell and Kuo CPSs show better agreement with the IMD data. Overall, Grell CPS has a close resemblance to the observation data with a minimum root mean square error, mean absolute error, lowest mean absolute percentage error (MAPE), and higher correlation coefficient. The model simulated results have also been investigated further using modified Nash Sutcliffe efficiency and modified Willmott's degree of index. These analyses confirm the potentiality of the Grell CPS followed by the Kuo CPS in simulating interannual variability of the surface air temperature over Indian and its five sub-regions. The MAPE in Grell and Kuo CPSs are 0.004 and 0.013°C during monsoon season over India, respectively. The inter-scheme difference in simulating surface air temperature is linked with the generation of low cloud convection and warming-induced atmospheric moisture advection in the schemes. Therefore, Emanuel, Tiedtke, and Mix98 CPSs have shown a persistent nature of overestimation in surface air temperature variability during JJAS. It is also inferred that after removing the systematic mean bias from the RegCM4.3 model simulated outputs; the skill of Emanuel, Mix98, and Mix99 could be useful over the Indian subcontinent except for the southern peninsular region. © 2021 Royal Meteorological Society
Statistical downscaling of maximum temperature under CMIP6 global climate models and evaluation of heat wave events using deep learning methods for Indo-Gangetic Plain
(John Wiley and Sons Ltd, 2024) Manisha Chaturvedi; Rajesh Kumar Mall; Saumya Singh; Pawan K. Chaubey; Ankur Pandey
The rising global temperature is one of the primary concerns of the world as it impacts the economy, environment and healthcare of any country which are more pronounced a regional level. Assessment of regional impacts of climate change at a local level requires fine resolution of climate data for which a robust and fast downscaling method is needed. In this study, we use three deep learning-based methods, namely long short-term memory network (LSTM), deep neural network (DNN) and recurrent neural network (RNN), to downscale CMIP6 13 GCMS models data (1.25° × 1.25° resolution) global climate model (GCM) maximum temperature (Tmax) at a regional scale of 0.5° × 0.5° spatial resolution for the period 1991–2010 over the Indo-Gangetic Plain (IGP). In addition to the temperature prediction, heat wave events have been also analysed in the study. The study found that LSTM method performs better than DNN and RNN in downscaling of all GCM model datasets when evaluated against observed maximum temperature data from the India Meteorological Department (IMD) in terms of RMSE (0.9–3.5), average of all grid MAE value between (1.2 and 2.68), correlation (0.68–0.9) along with and spatiotemporal variability. LSTM also performed better in heat wave prediction over the region with similar temporal range (12–36 events) and spatial occurrence as compared to the observation (12–28 events). Overall, the study concludes that LSTM performs better than two methods for Indo-Gangetic Plain with best hyper parameter tuning. Hence, we propose to utilize a deep learning framework based on LSTM for downscaling GCM dataset at a finer resolution. © 2024 Royal Meteorological Society.