India Meteorological Department Research Articles

Variability of Index of Coalescence Activity (ICA) over a rain-shadow region during monsoon and its role in cloud seeding programs in India

The Index of Coalescence Activity (ICA) is an indicator of the microphysical state of the cloud. ICA is related to cloud base temperature and buoyancy of cloud at 500 hPa level. It is a useful parameter for making a decision on the type of seeding to be carried out. During Cloud Aerosol Interaction and Precipitation Enhancement EXperiment (CAIPEEX) over the Indian region, aircraft operations for in situ measurements of aerosols and clouds were conducted from the base station Hyderabad to cover parts of north peninsular India falling under the rain-shadow region. Special Radiosonde flights were conducted in the afternoon hours prior to aircraft operations from the locations Hyderabad and Mahabubnagar in addition to regular India Meteorological Department (IMD) Radiosonde flights at 00 UTC from Hyderabad. The parameters like cloud base height and buoyancy at 500 hPa level obtained from Radiosonde flights have been used to estimate ICA values during the period of the experiment (June to October 2009–2011). The variability of ICA has been studied using morning and afternoon sounding profiles over the rain-shadow region. Morning hours showed highly negative ICA values, as compared to noon hours. During noon time, positive ICA values are observed before monsoon onset (in 2009, monsoon onset was 21st June over the study area) and on few occasions during post-monsoon (October). Positive ICA values represent that collision-coalescence (CC) process is not fully developed and hence, hygroscopic seeding is the proper decision. While, negative ICA values indicate active CC process, suggesting glaciogenic seeding. The soundings conducted just before the cloud seeding operations provide information about the actual thermodynamic and microphysical state of the cloud. Hence, ICA information is useful for providing guidelines to the operational cloud seeding programs.

An advanced GIS based approach for the assessment of coastal inundation in the storm surge region (Krishna District) of Andhra Pradesh, INDIA

Climate change leads coastal areas to suffer from rising sea levels, flooding, storm surges and cyclones which can cause irreparable harm to people and their assets. The mapping of these alterations to the coastline can assist in sustainable coastal development and surveillance. The current research emphasized the susceptibility of coastal inundation in the Krishna District of Andhra Pradesh, which is the State’s most vulnerable place to flooding and cyclonic storms. The Indian Meteorological Department website (mausam.imd.gov.in) was accessed to get latitudinal and longitudinal data on cyclonic storms that hit the area recently. The satellite imagery was acquired from Google Earth and USGS (earthexplorer.usgs.gov) to map the coastal settlements within 25 km radius of the coastline and to generate Digital Elevation Model (DEM) to identify the coastal inundation vulnerability up to 5.0 M contour elevation (2m, 3m, 4m and 5m). According to findings, there was a small impact of coastal inundation up to 4 meter sea level rise, so the research primarily concentrated on the assessment of coastal inundation with 5 meter sea level rise. A total of 512 settlements studied for coastal inundation encompassed an area of about 11405.4 hectares. The DEM identified that a large number of settlements in the Krishna District are on the verge of coastal inundation. About 125 settlements (24.5% settlements) with an area of approximately 2099.5 hectares are most vulnerable to coastal submergence, accounting for 18.5% of the studied area. This damage exposure assessment helps local government and policymakers to counteract extreme inundation events.

Extreme weather events induced mortalities in Jammu and Kashmir, India during 2010-2022

A study has been conducted on Extreme Weather Events (EWEs) induced mortalities in Jammu and Kashmir, India during 2010-2022. In the present study, we used the frequency of heavy rain, heavy snow, lightning/thunderstorm, Hailstorm and squall during the period 2010 to 2022 of 10 stations of J&K from India Meteorological Department. The mortalities occurred due to these extreme weather events for each district were collected from the Meteorological Centre Srinagar. The mean monthly precipitation and number of rainy days for each month was calculated for each station based on 40 years data (1982 to 2022). During the past 12 years, (2010-2022) a total of 2863 EWEs occurred over J&K in which 552 deaths occurred till 31st December 2022. Among the various EWEs, lightning (1942) and heavy rainfall (409) events were more frequent. When we compare the mortality per event, the heavy snow was more destructive compared to any other EWEs. The mortality per event due to heavy snow was highest (4.33) as compared to other extreme events, although the number of events of heavy snow is less (42) as compared to heavy rain (409), flash floods (168) and lightning (1942). District wise results of EWEs results revealed the highest deaths due to heavy snow were observed over Kupwara, Bandipora, Baramulla and Ganderbal. Similarly for flash floods, the highest deaths were observed over Kishtwar, Anantnag, Ganderbaland Doda. The Pearson correlation results revealed highest correlation of total deaths for heavy rain (0.77) and heavy snow (0.69) (significant at p value p<0.01) followed by flash floods (0.492) (significant at p value p<0.05). Negative correlation result was observed between heavy snow and windstorm (0.584) (significant at p value p<0.05). The present study has shown that, for the union territory as a whole, the heavy rain and heavy snow have been two major disasters causing mortality, though flashfloods, thunderstorms and windstorms are gaining importance. The trend analysis results also revealed that there is a significant increase in mortality over the years particularly due to flash floods (R2 value 0.434) and windstorm (R2 value 0.371).

Agro-climatic zone-wise drought hazards in Karnataka under historical and future climate scenarios

This study performed the spatio-temporal analysis of drought hazards across the agro-climatic zones (ACZs) of Karnataka under historical and future climate scenarios. The India Meteorological Department’s high-resolution gridded data for1989-2019 was used for historical drought occurrence analysis. Coordinated Regional Climate Downscaling Experiment ensemble data of the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios were used for analysing future drought hazards in the near (2031-2060) and end term (2061-2099) periods. The standardised precipitation index (SPI) was used to calculate the frequency of droughts at different accumulation periods of 1, 3, 6, 9, and 12 months. Subsequently, the ACZ-wise drought hazard index (DHI) was calculated and mapped geospatially using ArcGIS. The results indicated that moderate drought events have the highest frequencies of occurrence, followed by severe and extreme drought events for all accumulation periods. During 1989-2019, 54.8%, 28.3% and 16.7% of droughts were moderate, severe, and extreme, respectively. An increase of 2.6% and 2.4% in the frequency of moderate droughts is projected under the RCP4.5 and 8.5 scenarios, respectively, in the end term. Under both historical and future climate scenarios, a high frequency of extreme droughts was observed in the long accumulation periods (SPI-9 and SPI-12), whereas the frequency of moderate droughts was observed to be high in the short accumulation periods (SPI-1 and SPI-3). Under the historical scenario, the frequency of droughts in the extreme category was high in the southern transition, central dry, and north eastern dry zones, severe category in the northern dry, southern transition, and coastal zones, and moderate category in the north transition, hill, and southern dry zones. Among the 30 districts of Karnataka, Chitradurga, Udupi, Tumakuru, Ballari, Koppala, Raichuruand Gadaga districts have very high DHI. This study sheds light on the potential consequences of climate change on drought scenarios in the Karnataka state’s agro-climate zones and urges for zone specific drought adaptation and mitigation strategies to strengthen the State resilience.

Understanding the climatology and long-term trends in solar radiation using ground based in-situ observations in India

Understanding the variations of solar power potential over the country is essential for the optimum utilisation of solar energy in power generation, which demands accurate information of solar radiation and its variations. In the present study, we investigate the climatology and trends of global radiation (GR), diffuse radiation (DR), bright sunshine hours (BHS) and technical potential of solar power (Solar Photovoltaic potential; SPV potential) using in-situ data procured from India Meteorological Department for the period 1985-2019. GR is high (low) over the northwest and inland areas of peninsular (extreme north and northeast) India, whereas DR is high (low) over the coastal stations (extreme northern parts of the country). BHS is more (less) over northwest (north, northeast and southern peninsular) India. The country has SPV potential in the range of 1800-3400 Wm-2 with substantial regional variations. High (low) SPV potential is observed in the northwest regions (north, northeast and southern peninsular India). The GR and BHS (DR) have (has) a significant decreasing (increasing) trend in most parts of the country. However, the rate of decreasing (increasing) of GR (DR) has been weakened (strengthened) in the recent decade. The technical potential of solar power has a significant decreasing trend in most of the selected stations which is alarming. It necessitates the wide use of solar panels with better efficiency to meet the energy requirements from solar resources.

Anthropocene Air Quality Impact on the Kolkata People

Air quality is a significant concern globally, particularly in densely populated urban areas like Kolkata, pollution-wise the 2nd largest, fast-growing metropolis in India. The present study correlates to Kolkata city during the pre-Anthropocene (the 1950s) and the concurrent air quality related to human health. Investigating the impact of anthropogenic activities on air quality in Calcutta and Kolkata is relevant to understanding public health risks and environmental sustainability.
 The portrayal of the current air pollutants, causes, classification and measuring apparatuses recommended by the India Meteorological Department, collected from WBPCB, the levels of the parameters such as. Particulate Matter (PM2.5 and PM10), CO, CO2, NO2, O3 and SO2 etc. and analysed statistically.
 Air pollution has outnumbered the fatalities of other polluting agents such as waterborne, waste, radiation, and anthropogenic players in the 21st century. The novel insights into the specific effects of anthropogenic activities on air quality in Kolkata are the vulnerabilities of transportation, industry, meteorological and slum activities that are valuable. The vulnerabilities that distract ambient air quality indices have reached critical levels of air pollutants, especially particulate matter, GHG gases and NO2.
 Different zones in the city are residential, commercial, shanty towns, and industrial areas. Results show levels of pollution are different in each area. Seasonal considered, the winter season is found to have the highest distraction from ambient air quality. Some suitable measures are conceptualised that will mitigate the menace of air pollution to pave the way for bringing sustainable urban health and environment development to Greater Kolkata.

An analytical study on the effect of different photovoltaic technologies on enviro-economic parameter and energy metrics of active solar desalting unit

The enviro-economic and energy metrics analyses of the solar system by incorporating different photovoltaic technologies are required to fulfill the sustainable development goal of the United Nations. However, the effect of different photovoltaic technologies on the solar distillation system has not been informed by any of the researchers to date. This research gap has been explored in this study. The present manuscript is an endeavour to explore the best photovoltaic technology aimed at a single-slope solar desalting unit comprised of N identical photovoltaic thermal flat-plate-collectors. Data regarding all four weather conditions for a year for New Delhi's climate have been procured from the Indian Meteorological Department situated in the western part of India. Fundamental equations along with input data have been provided to a computational code developed in MATLAB for the estimation of various performance parameters. Concludingly, the energy payback time is minimum (1.64 years) for the case of copper indium gallium selenide photovoltaic technology, however, maximum values of life cycle conversion efficiency and net CO2 mitigation are respectively 0.171 and 199.10 tons of CO2 for the case of crystalline silicon photovoltaic technology. The carbon credit is also found to be a maximum (15269.25 US$) for crystalline silicon photovoltaic technology.

Relationship of ENSO and Standardized Precipitation Index (SPI) to Characterize Drought at Different Locations of Punjab, India

Drought is generally considered as a deficiency in precipitation over an extended period usually a season or more. It is a natural hazard that differs from other hazards since it has a slow onset, evolves over months or even years and its severity is often difficult to determine. Standardized Precipitation Index (SPI) is a widely used index to characterize drought on a range of timescales. In this study SPI was calculated annually and season (Kharif and Rabi) wise to correlate drought with ENSO events at different locations of Punjab viz. Amritsar, Ballowal Saunkhri, Bathinda, Ludhiana and Patiala. The historical data of rainfall for different locations of Punjab viz. Amritsar (1971-2017), Ballowal Saunkhri (1984-2018), Bathinda (1971-2018), Ludhiana (1971-2018) and Patiala (1971-2018) were collected from the Department of Climate Change and Agricultural Meteorology, PAU, Ludhiana and Met Centre, India Meteorological Department, Chandigarh. It was found that at all the locations very strong El Nino of 1997-98 resulted in above normal rainfall. Drought like conditions was observed during El Nino years as value of SPI was negative but with few exceptions. The SPI values were positive during La Nina and neutral years. Droughts during kharif season were found to be more related to El Nino years compared to La Nina years.

Application of Arc-SWAT Model for Water Budgeting and Water Resource Planning at the Yeralwadi Catchment of Khatav, India

Every facet of life, including human habitation, economic development, food security, etc., depends on water as a valuable resource. Due to the burgeoning population and rapid urbanization, water availability needs to be simulated and measured using hydrologic models and trustworthy data. To fulfill this aim, the SWAT model was processed in this work. The SWAT model was formulated to estimate the hydrological parameters of Yeralwadi using meteorological data from IMD (India Meteorological Department) for the period 1995-2020. The observed discharge data was collected from the HDUG Nasik group and used in the calibration and validation of the Model. The SWAT model was corrected & validated through the SUFI-II algorithm in SWAT-CUP to get a better result. The model’s sensitivity is checked by using statistical parameters like Nash-Sutcliffe Efficiency (NSE) and a coefficient of determination (R2). NSE values were 0.72 and 0.80 in calibration and validation, and R2 were 0.80 & 0.76 in calibration and validation, respectively, indicating the acceptance of the model. Results show that 40.6% of the total yearly precipitation was lost by evapotranspiration. The estimated total discharge from the Yeralwadi catchment was 55.6%, out of which 41.2% was surface runoff and 14.4% was baseflow. The other 17.8% was made up of percolation into confined and unconfined aquifers, which served as soil and groundwater storages. The surface runoff is influenced by Curve number (CnII), SOL_AWC, ESCO, and base flow was influenced by ALPHA-BF and GW_REVAP. This study will be useful to water managers and researchers to develop sustainable water resource management and to alleviate the water scarcity issues in the study basin.

Oxidative Stress Markers During Summer and Rainy Season in Deccani Sheep

The present study was designed to study the effect of two different seasons (summer and rainy) on the activities of some oxidative stress markers (SOD, GSHPx and MDA) in Deccani sheep. The experiment was carried out at the Department of Veterinary Physiology, KNP College of Veterinary Science, and Shirwal. Dist. Satara on eight apparently healthy, non-pregnant, non-lactating Deccani sheep above two years of age maintained under semi-intensive housing conditions at Livestock Farm Complex, KNP College of Veterinary Science, Shirwal, Dist. Satara. The meteorological data recorded during the present study were obtained from the Indian Meteorological Department, Pune. The monthly calculated mean values of THI during the study period revealed significantly (P < 0.01) higher THI during Summer (77.81 ± 0.19) than in the Rainy season (74.92 ± 0.16). In the present study, the mean plasma activity of SOD was significantly (P < 0.01) higher during the summer season (510.93 ± 24.77 U/ ml) than in the rainy season (436.58 ± 26.30 U/ ml). The mean erythrocyte activity of glutathione peroxidase showed significantly (P < 0.05) higher values in the summer season than in the rainy season (102.12 ± 5.03 U/gHb and 85.63 ± 3.89 U/gHb). The mean plasma levels of MDA were significantly (P < 0.01) higher during the summer season (5.29± 0.09 nmol/ml ) than in the rainy season (3.27± 0.09 nmol/ml). The finding indicates that oxidative stress markers like SOD, GSHPx and MDA could be used as a sensitive biomarker of oxidative stress caused due to heat stress in Deccani sheep.

Spatiotemporal assessment of rainfall variability and its characteristics over Rapti River Basin, India

Abstract The determination of rainfall characteristics of a basin is obligatory for mitigation planning and adaptation. This study presents the spatiotemporal analysis of gridded precipitation to understand the variability and seasonality over the Rapti Basin. This basin is affected by frequent flooding; hence it becomes necessary to understand the variability of rainfall. To carry out this analysis, daily precipitation data is extracted for 22 grids for 30 years (1990–2020) of high resolution. Extreme rainfall indices are computed as per the threshold decided by the Indian Meteorological Department (IMD). To determine the trend of the rainfall, Mann Kendall (MK) test, Modified MK test and Innovative Trend Analysis was used and magnitude of trend was determined using slope. Trends of daily rainfall, annual, and seasonal rainfall were determined. Annual rainfall has shown significant decreasing trend over the basin. Monsoon season has a noticeable tendency, but other seasons have only seen small shifts. From all the rainfall indices, PRCPTOT and SDII showed a decreasing trend with Sen Slope of −2.56 and 3.00 respectively. While examining the variance in precipitation across the basin, this study will be helpful.

Analysing spatio-temporal drought characteristics and copula-based return period in Indian Gangetic Basin (1901-2021).

Uncertainty and uneven distribution of monsoonal rainfall and its consequences on crop production is a matter of serious concern in India, specifically, in theIndo-Gangetic plainregion. In this study, drought patterns were investigated through standardised precipitation index (SPI) of varying timescales, using the India Meteorological Department (IMD) precipitation data (1901-2021). We analysed the spatio-temporal pattern of different drought characteristics (frequency, duration, severity, intensity) of the Indian Gangetic basin using run theory. The bivariate copula method has been incorporated to combine two drought properties (severity and duration). Copula integrates multivariate distribution and considers the dependency rate among the variables. The five most widely used copulas from various copula families, elliptical (normal, t-copula) and Archimedean (Clayton, Gumbel, Frank), were estimated for modelling, and the best fit copula was selected. The study revealed that seasonal drought is more frequent and intense in the Upper and Middle Gangetic Plain, whereas annual drought is quite scattered in nature. It is worthy to mention that downward drought trends were observed in this agricultural belts significantly after 1965; specifically, in the Upper, Middle, and Trans Gangetic Plain regions. With increasing drought duration and severity, the drought return period raised, but the frequency decreased gradually. Most of the droughts characterised by less duration and severity occurred with a return period below 10years for the whole region. The major 100 + years return period droughts were to be found after 1960 and their frequencies were significantly higher after 2000. The most recent remarkable droughts with more than 100years of return occurred during 2008-2011 and 2016-2018 in the Upper and Middle Gangetic plains, whereas in the Lower Gangetic plain, a hundred-year return period drought was occurred during 2010-2013. This study provides agroclimatic-zones-wise significant information of drought characteristics and its nature of occurrence in the Indian Ganga Basin. The results enhance the understanding of drought management and formulation of adaptive strategies to mitigate the adverse impact of droughts.

Land suitability analysis for turmeric crop for humid tropical Kerala, India, under current and future climate scenarios using advanced geospatial techniques.

Turmeric cultivation primarily thrives in India, followed by Bangladesh, Cambodia, Thailand, China, Malaysia, Indonesia and the Philippines. India leads globally in both area and production of turmeric. Despite this, there is a recognized gap in research regarding the impact of climate change on site suitability of turmeric. The primary objective of the present study was to evaluate both the present and future suitability of turmeric cultivation within the humid tropical region of Kerala, India, by employing advanced geospatial techniques. The research utilized meteorological data from the Indian Meteorological Department for the period of 1986-2020 as historical data and projected future data from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Four climatic scenarios of shared socioeconomic pathway (SSP) from the Intergovernmental Panel on Climate Change AR6 model of MIROC6 for the year 2050 (SSP 1-2.6, SSP 2-4.5, SSP 3-7.0 and SSP 5-8.5) were used. The results showed that suitable area for turmeric cultivation is declining in future scenario and this decline can be primarily attributed to fluctuations in temperature and an anticipated increase in rainfall in the year 2050. Notable changes in the spatial distribution of suitable areas over time were observed through the application of geographic information system (GIS) techniques. Importantly, as per the suitability criteria provided by ICAR-National Bureau of Soil Survey and Land Use Planning (ICAR-NBSS & LUP), all the districts in Kerala exhibited moderately suitable conditions for turmeric cultivation. With the GIS tools, the study identified highly suitable, moderately suitable, marginally suitable and not suitable areas of turmeric cultivation in Kerala. Presently 28% of area falls under highly suitable, 41% of area falls under moderately suitable and 11% falls under not suitable for turmeric cultivation. However, considering the projected scenarios for 2050 under the SSP framework, there will be a significant decrease in highly suitable area by 19% under SSP 5-8.5. This reduction in area will have an impact on the productivity of the crop as a result of changes in temperature and rainfall patterns. The outcome of the present research suggests that the state of Kerala needs to implement suitable climate change adaptation and management strategies for sustaining the turmeric cultivation. Additionally, the present study includes a discussion on potential management strategies to address the challenges posed by changing climatic conditions for optimizing turmeric production in the region. © 2024 Society of Chemical Industry.

Unusual subseasonal variability of Indian summer monsoon rainfall in 2020

AbstractAbove‐normal all‐India summer monsoon (ISM) rainfall was received in 2020 with 109% of its long‐period average. According to the India Meteorological Department (IMD), ISM 2020 experienced robust month‐to‐month rainfall variations; in particular, the peak monsoon month of July 2020 witnessed deficit rainfall. The present study investigated the possible mechanisms that are responsible for the July 2020 deficit rainfall. Analysis revealed that the strong low‐level moisture divergence caused by the westward‐propagating atmospheric Rossby wave, induced by suppressed western North Pacific (WNP) convection, is primarily responsible for the July rainfall deficit. The WNP suppressed convection is linked to anomalous low‐level anticyclonic circulation. It is noted that the intense WNP anticyclone is maintained by the tropical Indian Ocean (TIO) warming‐induced atmospheric Kelvin waves and strong low‐level convergence over the Meiyu‐Baiu rainband. Unlike July, the strength of the WNP anticyclone and TIO warming are weaker in June and August. In addition to the observational analysis, the skill of the Climate Forecast System version‐2 (CFSv2) model in predicting the July rainfall at a lead of about two months is examined. Strong positive rainfall anomalies over India are predicted by the model in July, which is in contrast to the observations. Despite predicting the TIO warming, the model failed to capture the westward extension of the WNP anticyclone, which impacted the July rainfall prediction. To understand the importance of the WNP anticyclone on ISM rainfall, a CFSv2 sensitivity experiment is carried out by imposing strong negative sea surface temperature (SST) anomalies over the WNP region to excite the anticyclone. Both the westward extension of the WNP anticyclone and suppressed rainfall over the core monsoon region in July are captured by the model when low‐level circulation is imposed. The model sensitivity experiment strongly supports the role of the WNP anticyclone and its westward extension in inducing reduced rainfall in July 2020 over the monsoon trough region.

Dual Data Streaming on Tropospheric Communication Links Based on the Determination of Beam Pointing Dynamics Using a Modified Ray-Based Channel Model

Tropospheric systems are widely used by military forces as they provide long-distance, real-time communication. Slow-fading propagation loss reduces link availability and limits its data-carrying capacity. Beam pointing dynamics provides knowledge of favorable heights at different times of the day in different seasons and a useful steering range. Beam steering, based on the beam pointing dynamics of the link, can overcome slow fading. The main contributions of this paper are the derivation of a realistic and accurate tropospheric channel model obtained by making important modifications to Dinc’s ray-based model. This paper also presents a method for determining beam pointing dynamics using the modified model. Beam pointing dynamics for two different links located in India have been determined in this paper using real-world data obtained from the Indian Meteorological Department. Another significant contribution of the paper is presenting the prospect of dual data streaming on tropospheric links using a fixed beam and a dynamically steered beam, based on the examination of beam pointing dynamics obtained for the two links. The main result presented in this paper is the comparison of powers received from the most favorable heights in a steerable beam system with the powers received in conventional fixed-beam systems for different days of the year. It has been shown that a higher received power can be achieved with beam steering. Another important result shown is the comparison of the achievable data rates for a single fixed-beam system and a dual-beam (one fixed beam and one dynamically steered beam) system. It has been shown that almost double the data rate is achievable in a dual-beam system. The method for the determination of beam pointing dynamics and the possibility of dual data streaming presented in this paper can significantly enhance the availability and capacity of tropospheric links.

Integrating conceptual and machine learning models to enhance daily-Scale streamflow simulation and assessing climate change impact in the watersheds of the Godavari basin, India

This study examined and addressed climate change's effects on hydrological patterns, particularly in critical places like the Godavari River basin. This study used daily gridded rainfall and temperature datasets from the Indian Meteorological Department (IMD) for model training and testing, 70% and 30%, respectively. To anticipate future hydrological shifts, the study harnessed the EC-Earth3 data, presenting an innovative methodology tailored to the unique hydrological dynamics of the Godavari River basin. The Sacramento model provided initial streamflow estimates for Kanhargaon, Nowrangpur, and Wairagarh. This approach melded traditional hydrological modeling with advanced multi-layer perceptron (MLP) capabilities. When combined with parameters like lagged rainfall, lagged streamflow, potential evapotranspiration (PET), and temperature variations, these initial outputs were further refined using the Sac-MLP model. A comparison with Sacramento revealed the superior performance of the Sac-MLP model. For instance, during training, the Nash Sutcliffe efficiency (NSE) values for the Sac-MLP witnessed an improvement from 0.610 to 0.810 in Kanhargaon, 0.580 to 0.692 in Nowrangpur, and 0.675 to 0.849 in Wairagarh. The results of the testing further corroborated these findings, as evidenced by the increase in the NSE for Kanhargaon from 0.890 to 0.910. Additionally, Nowrangpur and Wairagarh experienced notable improvements, with their NSE values rising from 0.629 to 0.785 and 0.725 to 0.902, respectively. Projections based on EC-Earth3 data across various scenarios highlighted significant shifts in rainfall and temperature patterns, especially in the far future (2071–2100). Regarding the relative change in annual streamflow, Kanhargaon projections under SSP370 and SSP585 for the far future indicate increases of 584.38% and 662.74%. Similarly, Nowrangpur and Wairagarh are projected to see increases of 98.27% and 114.98%, and 81.68% and 108.08%, respectively. This study uses EC-Earth3 estimates to demonstrate the Sac-MLP model's accuracy and importance in climate change water resource planning. The unique method for region-specific hydrological analysis provides vital insights for sustainable water resource management. This research provides a deeper understanding of climate-induced hydrological changes and a robust modeling approach for accurate predictions in changing environmental conditions.

Numerical-model-derived intensity–duration thresholds for early warning of rainfall-induced debris flows in a Himalayan catchment

Abstract. Debris flows triggered by rainfall are catastrophic geohazards that occur compounded during extreme events. Few early warning systems for shallow landslides and debris flows at the territorial scale use thresholds of rainfall intensity–duration (ID). ID thresholds are mostly defined using hourly rainfall. Due to instrumental and operational challenges, current early warning systems have difficulty forecasting sub-daily time series of weather for landslides in the Himalayas. Here, we present a framework that employs a spatio-temporal numerical model preceded by the Weather Research And Forecast (WRF) Model for analysing debris flows induced by rainfall. The WRF model runs at 1.8 km × 1.8 km resolution to produce hourly rainfall. The hourly rainfall is then used as an input boundary condition in the spatio-temporal numerical model for debris flows. The debris flow model is an updated version of Van Asch et al. (2014) in which sensitivity to volumetric water content, moisture-content-dependent hydraulic conductivity, and seepage routines are introduced within the governing equations. The spatio-temporal numerical model of debris flows is first calibrated for the mass movements in the Kedarnath catchment that occurred during the 2013 North India floods. Various precipitation intensities based on the glossary of the India Meteorological Department (IMD) are set, and parametric numerical simulations are run identifying ID thresholds of debris flows. Our findings suggest that the WRF model combined with the debris flow numerical model shall be used to establish ID thresholds in territorial landslide early warning systems (Te-LEWSs).

Fidelity of WRF model in simulating heat wave events over India

The evaluation of Weather Research and Forecasting (WRF) model has been performed for simulating episodic Heat Wave (HW) events of 2015 and 2016 with varied horizontal resolutions of 27 km for the entire India (d01), 9 km for the North West (NW (d02)) and South East (SE (d03)) domain. Study compares the maximum temperature (Tmax) simulated by WRF model, using six different combination of parameterization schemes, with observations from the India Meteorological Department (IMD) during the HW events. Among the six experiments, Exp2 (i.e., combination of WSM6 microphysics (MP) together with radiation parameterization CAM, Yonsei (PBL), NOAH land surface and Grell-3D convective schemes) is found closest to the observations in reproducing the temperature. The model exhibits an uncertainty of ± 2 °C in maximum temperature (Tmax) for both the regions, suggesting regional temperature is influenced by the location and complex orography. Overall, statistical results reveal that the best performance is achieved with Exp2. Further, to understand the dynamics of rising HW intensity, two case studies of HW days along with influencing parameters like Tmax, RH and prevailing wind distribution have been simulated. Model simulated Tmax during 2015 reaches up to 44 °C in NW and SE part of India. In 2016, HW is more prevailing towards NW, while in SE region Tmax reaches upto 34–38 °C with high RH (60–85%). The comparative research made it abundantly evident that these episodic events are unique in terms of duration and geographical spread which can be used to assess the WRF performance for future projections of HW.

Statistical downscaling of maximum temperature under CMIP6 global climate models and evaluation of heat wave events using deep learning methods for Indo‐Gangetic Plain

AbstractThe 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.

Intensity duration and frequency of Heat wave in different phases of MJO over India

The Madden-Julian Oscillation (MJO) is an important tropical atmospheric phenomenon that plays a significant role in modulating weather patterns across India. Heat Waves (HW) are extreme weather events that have severe impacts on human health, agriculture, and various socio-economic sectors. This research paper aims to investigate the dynamical relationship between MJO phases, as measured by the Real-time Multivariate MJO (RMM) index, and the occurrence of HW in India, as defined by the Indian Meteorological Department. This paper demonstrated the relationship among three aspects, i.e., intensity, duration, frequency of HW during the different MJO phases. The differential dynamics associated for about two-fold rise in the HW occurrence in the South East, Eastern and North West India in the MJO active phase. Inactive phase witnessed enhancement (suppression) of HW during wet (dry) over the regions. The novel finding of this paper is that, the weaker the MJO, the higher probability of HW occurrence in respective regions depending on the position of the MJO convection center. The maximum duration is observed to be significantly decreased by about 2 days in the active MJO phase over NW and north central parts of the country. It is also inferred from this study that there is a significantly decrease of 0.5 °C in HW intensity during the active wet phase of MJO over the NW India. To explain the linkage of HW with MJO, geopotential height (GPH), wind circulation, velocity potential (VP), sea surface temperature (SST) and outgoing longwave radiation (OLR) composite anomalies have been computed and used in the analysis. It is observed that all these parameters have a direct impact on the evolution, strength and spread of the HW in India. This dynamical study mechanism can be prescribed in the numerical weather prediction models for the improved prediction of such extreme weather events during summer for proactive disaster management.