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The present study explored district-wise monthly rainfall of two climatic regimes (1960–1990 and 1991–2020) of the West Bengal state of India, which receives an annual rainfall of about 1500 mm with huge seasonal rainfall variability. A simple time series analysis does not show any noticeable trend. However, decomposing rainfall parameters into several indicators may explore the change. From this perspective, the present paper intends to explore the nature, degree, and direction of rainfall alteration. The range of variability approach (RVA) considered 29 indicators was adopted for unveiling it. RVA explored that in the 1991–2017 phase, the rainfall failure rate above the upper threshold (75th percentile) is 33% which is 10% greater than in 1961–1990. Seasonal variation in failure rate was detected, but overall larger area of the state recorded failure rate (FR) above the threshold. This result leads to increasing wetness conditions. Very low to moderate (< 0.4) degree of impact of rainfall alteration (DIRA) was found in the recent period. The areal occupation of moderate DIRA was considerable in pre-monsoon, post-monsoon, and winter seasons. Time series rainfall data-based flow duration curves (FDCs) indicated the dominance of the eco-surplus rainfall state over greater parts of the state (72–84%) in all the seasons except winter (49%). Such surplus rainfall may be beneficial to the ecological and agricultural perspective of the state. [ABSTRACT FROM AUTHOR]

Better understanding on the phases of Indian summer monsoon i.e., onset, progress and withdrawal helps in the decision making and proper management. In this paper, date of onset of monsoon (DOM) and date of withdrawal of monsoon (DWM) is estimated using the long-term daily rainfall data observed by the India Meteorological Department (IMD) and cumulative rainfall anomaly concept, as the onset/withdrawal represents a signature of rainfall transition. The results are promising with a better match between the estimated DOM and IMD announced DOM over Kerala. The length of monsoon season variability is also analysed. The concept of onset at grid point (OGP) in India is also evaluated and progress of monsoon is monitored by considering the latitudinal variation, persistent & strength of rainfall in the continental India. The zonal classification of the spread of monsoon in terms of the intensity and time (duration) is presented. Zone wise pentad analysis of onset clearly signifies the progress of monsoon and its dependency on various physical factors like SST, OLR, TTG, ITCZ, Energy budget and wind circulation. The impact of ENSO on the OGP is brought out, which clearly shows that DOM, DWM and progress is almost influenced by the largescale processes. The intensity–duration–frequency analysis quantified the actual ISM length, seasonal rainfall and rainy days at all locations in India. This study will help modellers in better understanding of onset and progress of monsoon dynamics in India and can be integrated with NWP for the accurate and advance prediction of DOM and the progress. [ABSTRACT FROM AUTHOR]

The east coast of India is highly prone to devastating winds, torrential rainfall, and storm surges caused by tropical cyclones. The storm surge is affected by ocean basin characteristics involving the width and slope of the continental shelf. The bed roughness plays a major role in surge formation. The east coast of India is characterized by a broader shelf in the north and a narrow shelf in the south. This paper uses a hybrid Finite Volume Method–Finite Element Method based Shallow water equation (SWE) solver to predict the storm surges during different cyclone events, and the roughness parameter Manning's n is used in bed friction calculations. The bottom friction coefficient parameterization involving bed roughness is used to calibrate the resistance to flow in the numerical model. The calibration exercises are carried out with different values of n for each surge simulations for different cyclones to predict the surface elevation. Different statistical parameters against the measured values are used to analyze the impact of varying n values on predicted surge levels, and the most suitable n value is carefully chosen. The relationship between n and the bed slope is established as an expression, to replace the formulations involving Manning's n, thereby minimizing the usual computational efforts. The performance of the novel bed friction formulation involving the physical parameter in bed slope is demonstrated through statistical evaluations. [ABSTRACT FROM AUTHOR]

The mountainous areas are vulnerable to climate change and may have many socio-economic and environmental implications. The changing pattern of meteorological variables has deleterious effects on natural resources and livelihood. This paper makes an attempt to analyse trend and forecast metrological variables in Nainital district of India. Monthly, seasonal, and annual trends in rainfall and temperature were examined by Modified Mann–Kendall during 1989–2019. The magnitude of trend in temperature and rainfall was determined using Sen's slope estimator. Ensemble machine learning model was utilized for forecasting the variables for the next 16 years (2020–2035). The effectiveness of the model was examined through statistical performance assessors. The results revealed a significant increasing trend in the rainfall (at the rate of 9.42 mm/year) during 1989–2019. Increasing trend in the mean, minimum, and maximum temperatures on an annual basis was observed in the district. A remarkable increase in the rainfall and temperature was forecasted during various seasons. The findings of the study may help the stakeholders in devising suitable adaptation measures to climate variability. The bagging approach has shown its effectiveness in forecasting meteorological variables. The other geographical regions may find the methodology effective for analyzing climate variability and lessening its impact. [ABSTRACT FROM AUTHOR]

Climate change is a major concern, as it profoundly affects many facets of our lives. It has brought about several issues, including declining water supply, reduced agricultural yields, increased drought occurrences, and increased heat waves. Amidst these challenges, the influence of short-term drought events on plant growth and irrigation schedule emerges as a critical concern. However, despite these evident consequences, a nuanced understanding of the intricate relationship between the severity and duration of short-period drought/deficit events still needs to be explored. This paper analyses fortnightly water deficit periods over different regions of India, which would be more relevant to re-scheduling the irrigation events than monthly or longer duration in preventing crops from reaching the permanent wilting point. Hence, this work considers analyzing 15-day Standardized Precipitation Index (SPI) trends and drought characteristics using conventional methods and Innovative Trend Analysis (ITA) techniques. The analysis uses gridded rainfall data from the India Meteorological Department (IMD), which has a spatial resolution of 0.250-E and 0.250-N. The data spans the period from 1970 to 2021. The ITA and Mann Kendall (MK) displayed nearly identical areas of increasing and decreasing trends, but ITA effectively identified significant trends. While MK and Modified Mann Kendall (MMK) could only indicate significant trends for 12.94%, 9.57%, and 9.9% of grid points for SPI, drought severity, and duration, respectively, ITA was able to identify significant trends at 44.31%, 10.9%, and 10.1% on an annual scale. The ITA method effectively identified the significant trends and magnitudes of fortnightly SPI and drought characteristics. The Tropical monsoon (Am), Tropical savannah (Aw), Arid desert hot (BWh), Arid steppe hot (BSh), and Temperate dry winter warm summer (Cwb) climatic zones have shown a significant increase in annual drought severity. Similarly, a significant increase in monsoon drought severity is observed across several states, including Gujarat and Mizoram, impacting diverse geographic extents. The present study can help policymakers and water resource managers decide on water allocation, irrigation, and crop management practices. [ABSTRACT FROM AUTHOR]

Under global warming and climate change (CC), the vulnerable piece of land is India's Eastern-Himalayan-Region (EHR), where millions' livelihood activities are at risk. Quantifying the degree of CC-induced vulnerability over the highly climate-sensitive mountain zone is challenging due to data inadequacy. This paper attempted to assess the CC perception of kiwifruit growers under EHR and the impact of CC on its cultivation. Perceived and scientific data were used for accurate decision-support information. The climatic variables of two locations were analyzed for trend using Mann–Kendall's test. Results suggested that the rainfall trend was found non-significant for both locations, but there was a negative trend during winter for Pasighat and during all seasons for Itanagar. Maximum temperature exhibited an increasing trend for annual, and minimum temperature showed an increasing trend across seasons for the Pasighat region. For the Itanagar region, the maximum temperature was found to have a significantly increasing trend in all seasons, while the minimum temperature showed an increasing trend during winter and annual seasons. Morning relative humidity showed an increasing trend for annual data for Pasighat, while annual evening relative humidity was significantly increased for Itanagar. The study shows that the indigenous farming communities were following shifting cultivation and diversifying their traditional agriculture to kiwifruit cultivation as an adaptation strategy. The community had a clear perception of CC trends and an inadequate perception of the impact on agriculture, therefore, engaging indigenous communities and other stakeholders to postulate a comprehensive adaptation and mitigation strategy for CC. [ABSTRACT FROM AUTHOR]

The Indian economy and food grain production are intricately tied to the spatial distribution and the amount of rainfall during the summer monsoon period (June to September). Numerous studies have delved into modeling methods for forecasting Indian summer monsoon rainfall. This paper utilizes monthly rainfall data spanning 72 years (1950–2021) to analyze, model, and predict the monsoon over Northwest India using Dynamic Linear Modeling (DLM). In view of this, the well-accepted dynamic linear model with significant components is selected from the parsimonious models of DLM based on Bayesian Information Criteria (BIC), significant tests, and statistical fit. The dynamic linear model comprises a significant linear time trend component, a deterministic trigonometric seasonal component, a deterministic cycle component, and a stochastic irregular component has been selected. The validation of the selected model passed through the normal and correlation diagnostics of model residuals. The analysis reveals a subtle decreasing trend in monsoon rainfall, showcasing consistent behaviour in deterministic seasonality, and highlighting the deterministic nature of cyclical component with a period of approximately 2.5 years, alongside irregular variations. Utilizing this model, future monsoon rainfall over Northwest India has been forecasted for the years 2022–2027, providing a 95% confidence interval. [ABSTRACT FROM AUTHOR]

Indian southwest monsoon rainfall has a huge socioeconomic influence, and any changes in monsoon rainfall patterns will have major consequences for agriculture, water supplies, and other connected sectors of economy. The paper explores the charactering and forecasting of the southwest monsoon rainfall (June to September) patterns over the Central region of India for the period (1901–2021: 121 years) using linear state space modeling. The study period is split into two periods, i.e., earlier period (EP: 1901–1950) and the recent period (RP: 1951–2021). The linear state space model (LSSM) with significant latent components such as trend, seasonal, periodic and random variations or noise is selected for modeling the southwest monsoon rainfall (SWMR) based on Bayesian information criteria (BIC) and statistical fit for the two periods. The result indicates that an increase in linear time trend during the EP and the RP has a decreasing local linear time trend in SWMR, and it is interesting to notice that the EP had no noteworthy periodic variations, whereas the RP exhibits significant periodic variations in SWMR. Further, the selected LSSM forecasts SWMR patterns for the period 2022–2028, and it has been observed that the Central Indian region is expected to receive substantial rainfall in the years 2026, 2027 and 2028. [ABSTRACT FROM AUTHOR]

Devising effective strategies to retard the negative impacts of extreme climatic events on water security in general and environmental water balance in specific is imperative, especially in semi-arid rural-Western-India, where climate change has markedly increased water scarcity in domestic and agricultural sectors. This exploratory research thus aimed at investigating the water budget against mismanagement of available water resources for remote-marginalized villages in rural-Nashik (West-India). Investigation entailed visual interpretation of LULC analysis (2008–2017) using fine-scale Google Earth imageries, rainfall-runoff analysis using SCS-CN method, determining the supply-demand potential of water storage structures using field-experimentations and PRA tools, and estimation of season-wise water-budget using hydrological (water-balance) equations. Results identified agriculture as the major land-use (~ 78%) followed by hilly regions (~ 16%) and water bodies (~ 3%). Surface runoff was found as the dominating hydrological phenomenon (33–89% of monsoonal rainfall) followed by surface storage (6–36% of annual rainfall) and groundwater flow (4–82% of annual rainfall). Deforestation over hills for firewood, which reduced forest cover by 52%, was observed as the primary concern for soil erosion, thereby siltation of water bodies and higher runoff against percolation. Erratic rainfall patterns (departure range − 42 to 83% during 1998–2017) coupled with sub-graded irrigational systems confined the agricultural practices to one season [South–West monsoon (June–September)]. High unmet drinking water demand (73% to 98%) forced women communities to fetch water by walking 4.5 ± 1.5 km. Considering the urgent response to collectively combat water-budget imbalance, integrated water security plans were proposed using decentralized participatory watershed development approaches. [ABSTRACT FROM AUTHOR]

Water is an important part of global circulation. Precipitation is the principal input into these cycles, and its measurement and evaluation in spatial and temporal scales are necessary. This paper assessed the performance of seven satellite precipitation products, i.e., Climate Hazards group Infrared Precipitation with Stations, Princeton Global Forcing's, Tropical Rainfall Measuring Mission, Climate Prediction Center, Climate prediction center MORPHing technique, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record, Multi-Source Weighted-Ensemble Precipitation using India Meteorological Department gridded precipitation as a reference from 1998 to 2016 over the Godavari River basin, India, applying continuous and categorical metrics. The Nash–Sutcliffe efficiency, coefficient of determination, and root mean square error for Multi-Source Weighted-Ensemble Precipitation were 0.806, 0.831, and 56.734 mm/mon and for Tropical Rainfall Measuring Mission were 0.768, 0.846, and 57.413 mm/mon. Similarly, categorical metrics, i.e., highest accuracy, Peirce's skill score, and lowest false alarm ratio, were recorded by Multi-Source Weighted-Ensemble Precipitation with 0.844, 0.571, and 0.462, respectively. Cumulative distribution function was also assessed for all the datasets, representing all products that overestimated low to medium precipitation events except Multi-Source Weighted-Ensemble Precipitation and followed a similar pattern of India Meteorological Department except for low precipitation events. All satellite precipitation products were estimated accurately for low-lying areas and inaccurately over low precipitation regions, large forests, and hilly regions. Overall, Multi-Source Weighted-Ensemble Precipitation and Tropical Rainfall Measuring Mission datasets performed better in evaluating precipitation, and these datasets can be used in hydrological applications. [ABSTRACT FROM AUTHOR]

The rainfall over the Indian region, governed majorly by the monsoonal flow, is a point of research in the perspective of climate change. In this paper, we compute the change points in the rainfall series at every grid of the India Meteorological Department (IMD) daily gridded rainfall data for a period of 120 years (1901 to 2020). The map shows clearly demarcated regions indicating different zones, where the rainfall statistics have altered at different periods. It is observed that in a major part of central India, the shift in rainfall intensity is mainly associated with the time frame 1955–1965; in the Indo-Gangetic plain, the changes are found to be more recent (1990), while the latest changes (post 2000) are observed particularly for North Eastern region and some parts along the East Indian coast. The changeover years are significant at a 95% confidence level for most part of the Indian landmass. The causes may be surmised due to moisture transport from the Arabian Sea (Central India), the presence of aerosol (Gangetic Plain), and the possible revival of monsoon due to land–ocean gradient (Eastern coast and North East India). This is the first-ever study which provides a comprehensive daily rainfall change point map over India using 120 years of gridded station data. [ABSTRACT FROM AUTHOR]

The present research study aims to analyze flood frequency to relate flood magnitudes with corresponding return periods. These estimates are crucial for the development of flood-preventive hydrological infrastructures and flood plain zoning, etc. Basin-wide high intensity of rainfall and high discharge from the upper riparian region during monsoon months creates flooding in the middle and lower reaches of the Ghagra River basin. Therefore, the flood estimates for three gauging sites situated on the mainstream of the Ghagra River viz., Elginbridge, Ayodhya, and Turtipar, have been comprehended using the Gumbel distribution (Extreme Value Type I) method and the Log-Pearson Type III distribution method. Flood estimates are calculated for 1.01, 1.05, 1.11, 1.25, 2, 5, 10, 20, 50, 100, 200, and 500 years return periods considering flood time series of 50 years to reduce estimation uncertainty. The findings of flood frequency analysis (FFA) revealed that the probability of occurrence of the flood is more than 80% at all three sites because Ghagra River can carry around 7800 to 9000 m3/s of water discharge without posing a high risk of levee break. The upper and lower limits of discharge carrying capacity depend on the river's desiltation process. Hence, the flood occurs almost every year in the basin; its only variation is its severity. The rainfall-runoff relationship is estimated by integrating a simple linear model and for rainfall trend analysis Mann-Kendall is applied. Linear regression analysis-based rainfall-runoff relationship outcomes revealed a significant relationship with a positive correlation coefficient i.e., 0.2722 for Elginbridge, 0.39624 for Ayodhya, and 0.4844 for Turtipar gauging site in the monsoon season but other factors like a high amount of water discharge from dams in upper riparian regions, etc. are also responsible for flooding in the middle and lower reaches. The Mann-Kendall trend analysis shows a decrease in annual average rainfall and rainfall in the monsoon season. Discharge variability indicates the direct relationship between flood fury and changes in climatic patterns in recent decades. This paper identifies that future research is needed to better inform the policy planners who strive to design sustainable infrastructure. [ABSTRACT FROM AUTHOR]

Evaluating drought parameters at the basin level is one of the fundamental processes for planning sustainable crop production. This study aimed to evaluate both short-term and long-term meteorological drought parameters within the Vaippar Basin, located in southern India, by employing the standardized precipitation index (SPI). Gridded rainfall values developed from 13 rain gauge stations were employed to calculate the SPI values. Drought parameters, encompassing occurrence, intensity, duration, frequency, and trends, were assessed for both short-term and long-term droughts. The study findings indicated that the occurrence of short-term drought was 51.7%, while that of long-term drought was 49.82%. Notably, the basin experienced extreme short-term droughts in 1980, 1998 and 2016 and long-term droughts in 1981, 2013, and 2017. Utilizing an innovative trend identification method for SPI values, a significant monotonic upwards trend was identified in October and December for short-term drought and in December for long-term drought. This study defined the minimum threshold rainfall, which represents the critical amount required to prevent short-term drought (set at 390 mm) and long-term drought (set at 635 mm). The drought severity recurrence curves developed in this study indicate that when the SPI values fall below − 1.0, short-term drought affects 25% of the basin area, while long-term drought impacts 50% of the basin area at a 20-year recurrence interval. Additionally, the drought hazard index (DHI), which combines drought intensity and severity, demonstrated higher values in the northwestern regions for short-term drought and in the southern areas for long-term drought. The study's findings, highlighting areas of drought vulnerability, severity, and recurrence patterns in the basin, direct the attention for timely intervention when drought initiates. [ABSTRACT FROM AUTHOR]

The increasing concentrations of greenhouse gas emissions in the atmosphere along with the increasing temperatures exacerbate the situation, making it one of the major issues for food security. This research aims to explore the effects of climate change on food production in India. More specifically, this study investigates the impact of temperature, rainfall, CO2, methane, and N2O emissions and energy use on food production using a time spanning from 1990 to 2019 and explores the major critical factors impacting food production in India. The study used autoregressive distributed (ARDL) estimates for long-run and short-run cointegration. The empirical results demonstrated that energy use, rainfall, and methane emissions improve food productivity, whereas temperature and N2O emissions undermine food production in the long run and short run. However, the results revealed that CO2 emissions do not affect food production. Additionally, we used the dynamic ordinal least squared approach to verify the robustness of ARDL results. The results also demonstrate that N2O emissions and temperature are critical determinants of food production, indicating that a rise in temperature and N2O emissions significantly reduces food production in India. On the other hand, the findings show that methane emissions and rainfall significantly increase food production with a coefficient of 0.19 and 0.059, respectively. The most important adaptation strategies that should be considered due to climate change are reducing greenhouse gas emissions, expanding renewable energy investment, and adopting new technologies to combat environmental challenges. [ABSTRACT FROM AUTHOR]

Understanding the trend of seasonal rainfall in the context of climate change is crucial for the maintenance of regional water resources management. The present study examines the seasonal rainfall trend in the Mahi River basin, India by using the wavelet transform and clustering method. Daily gridded rainfall data (0.25° × 0.25° spatial resolution) for 51 grids from 1970 to 2020 have been taken from India Meteorological Department (IMD). The monthly, seasonal, and annual statistic has been analyzed for 51 years. We have also plotted the relationship between precipitation trend–elevation. After that, homogeneous precipitation regions are delineated with hierarchical clustering analysis. Results reveal that seasonal precipitation over the basin clusters into 4 subregions for monsoon and 3 subregions for winter, pre-monsoon, and post-monsoon seasons. After the regionalization of the subregions, the periodicity and the inter-seasonal relationship were analyzed using continuous wavelet transform (CWT). In addition, it was clear that the cross-correlation between pre-monsoon and monsoon seasons had a significant periodic change in the last 30 years over the basin. The basin is located adjacent to the Arabian sea, which makes the basin more sensitive to a natural event like ENSO, it can cause changes in ocean temperatures and atmospheric circulation patterns that affect precipitation patterns during the study period. It is expected that some natural phenomena like monsoon variability, low-level jet streams, and many more also have an impact on the rainfall patterns of the basin. Thus, the understanding of seasonal precipitation variation provides a practical reference for water resources management, agricultural planning, and a forecast of precipitation in different regions and river basins of India which may give a better climate change indication. [ABSTRACT FROM AUTHOR]

India experienced several unprecedented floods in the recent decades. The increase in the extreme rainfall events (EREs) is the primary cause for these floods, manifesting its societal impacts. The daily downscaled and bias corrected (DBC) Coupled Model Intercomparison Project Phase 6 (CMIP6) rainfall and sea surface temperature (SST) are prepared for the Indian region and are utilized to examine the characteristics of EREs. The DBC products capture the characteristic features of EREs for the baseline period, which inspired us to assess the EREs over India in CMIP6 future projections. Consistent with the observations, DBC product shows ~ 8% of Indian land found to experienced extremely heavy rainfall associated with the long duration EREs in the baseline period. However, area and extreme rainfall thresholds are projected to increase by about 18(13)% and 58(50)%, respectively in the far future under SSP5-8.5 (SSP2-4.5) emission scenario relative to the baseline period. A two-fold-65(62)% increase in long-duration EREs compared to the short-duration EREs and substantial warming ~ 2.4(2.9) oC of Indian Ocean SSTs in the far future under SSP5-8.5 (SSP2-4.5) emission scenario compared to baseline period are reported. These findings may provide fundamental insights to formulate national climate change adaptation policies for the EREs. [ABSTRACT FROM AUTHOR]

Every year, many farmers die as a result of snakebites or other envenomations all over the world. There is currently a need to identify the snakebite regions that cause severe damage in the Indian farmer's family, especially in the study area. The study area, i.e., India's Krishna district, is highly prone to snakebites and floods. It best suits the high yield of agricultural crops due to fertile lands and rich water resources. The present research focuses on the identification of the snakebites-prone areas using geospatial analytics based on the Area Land (AL), Rainfall (RF), Flood Zone (FZ), Snakebite (SB), River Side (RS), and Locality (L). A novel Snake Bite Impacted Area (SBIA) method is proposed and implemented to find the Geospatial predictive correlation analysis of snakebites. To measure the performance of SBIA, various popular data classification methods are tested, such as multi-class SVM, Decision Trees (DT), Boosted Trees (BT), Linear Discriminate (LD), and K-Nearest Neighbor (KNN). Among all popular techniques, the SBIA performance is high with an accuracy of 94.12, whereas the accuracy of LD is 76.5, then both SVM and KNN are 82.4, whereas DT and BT are 94.1. In addition the AUC result of SBIA is compared with existing Ecological Ninche Models to predict snakebite prone areas with location. Overall, the performance of SBIA is good, which combines spatial and non-spatial data to predict the snakebite-prone areas. [ABSTRACT FROM AUTHOR]

A dense network of rain gauges and considerably large variability of the southwest monsoon precipitation across the country make India a suitable test-bed to evaluate any satellite-based precipitation product. In this paper, three real-time infrared-only precipitation products derived from the INSAT-3D satellite namely, INSAT Multispectral Rainfall (IMR), Corrected IMR (IMC) and Hydro-Estimator (HEM) and three rain gauge-adjusted Global Precipitation Measurement (GPM)-based multi-satellite precipitation products namely, Integrated Multi-satellitE Retrievals for GPM (IMERG), Global Satellite Mapping of Precipitation (GSMaP) and an Indian merged satellite-gauge product (INMSG) have been evaluated over India at a daily timescale for the southwest monsoon seasons of 2020 and 2021. An evaluation against rain gauge-based gridded reference dataset shows noticeable reduction of bias in IMC product over IMR, primarily over the orographic regions. However, INSAT-3D infrared-only precipitation retrieval algorithms have limitations in shallow and convective precipitation estimation. Among rain gauge-adjusted multi-satellite products, INMSG is shown to be the best product in the monsoon precipitation estimation over India due to use of rather larger number of rain gauges than IMERG and GSMaP products. All satellite-derived precipitation products, i.e. infrared-only and gauge-adjusted multi-satellite products underestimate heavy monsoon precipitation by 50–70%. The bias decomposition analysis indicates that a simple statistical bias correction would considerably improve the performance of the INSAT-3D precipitation products over the central India, but the same might not work over the west coast due to rather larger contributions of both positive and negative hit bias components. Although rain gauge-adjusted multi-satellite precipitation products show very small or negligible total biases in the monsoon precipitation estimation, positive and negative hit bias components are considerable over the west coast and central India. Furthermore, rain gauge-adjusted multi-satellite precipitation products underestimate very heavy to extremely heavy precipitation with larger magnitudes than the INSAT-3D derived precipitation products over the central India. Among the rain gauge-adjusted multi-satellite precipitation products, INMSG has smaller bias and error than IMERG and GSMaP products for very heavy to extremely heavy monsoon precipitation over the west coast and central India. Preliminary results of this study would be useful for end users in choosing a better precipitation product for real-time and research applications as well as for algorithm developers in further improving these products. [ABSTRACT FROM AUTHOR]

Quantification of rainfall intermittency via. interevent time distribution, series of continuous wet spells (burst size) and variability in interevent times between rainfall events is essential for planning and management of water resources and hydrologic extremes. However, their structure, quantification and association with long-term climatology are less explored. In this paper, a complex system-based measure – burstiness – is used to quantify the variability of interevent times across six meteorologically homogenous zones of India. It is observed that burstiness is related to the burst size as well as long-term rainfall climatology. The existence of unimodal and bimodal structure in burstiness distribution reveals the uniqueness and differences in the rainfall patterns. The differences in sensitivity of rainfall to burstiness highlight the role of interplay between climate-landscape and reveals the importance of the intermittent structure of rainfall. The study provides an approach to model intermittency by preserving the temporal structure of the interevent time distribution. [ABSTRACT FROM AUTHOR]

In a mountainous basin, flash-flood-induced soil erosion and its assessment are of major concern to the researchers. Cloudburst-induced extreme rainfall, undulating terrain, and inadequate water and soil conservation practices aggravate the problem of soil erosion. Alaknanda, a major basin of the upper Ganges in India, has experienced several incidents of flash floods. These have resulted in heavy soil loss, water siltation, and pollution in the basin. This study uses the revised universal soil-loss equation (RUSLE) in the GIS framework to assess the soil loss rate in various sub-basins of the Alaknanda. Further, the study suggests sub-basin prioritisation based on high soil erosion rate and past vulnerability that need attention of the decision-makers. RUSLE, an empirical soil erosion model, is easy to comprehend physically, involves minimum resources, and works on readily available inputs. The parameters used in the assessment include R-Rainfall Erosivity, Cover Management-C, Topographic-LS, Conservation Practice-P, and Soil Erodibility-K. The average R, K, LS, C & P factors for the Alaknanda basis are estimated as 437 MJ mm/ha/yr., 0.07 t ha h MJ-1 ha-1 mm-1, 1.4, 0.7, and 0.62, respectively. The mean annual soil erosion rate in the sub-basins varies from 6 to 27.9 t /ha/year with a mean rate of 13.4 t/ha/year and total erosion of 13.7 Mt/year. The results show that out of the twelve sub-basins, two (WS7 and WS5) fall in the high erosional class (18–30 t/ha/year), nine fall in the moderate erosional class (7–18 t /ha /year) and one basin in the low erosional class (1.0–7.0 t /ha/year). It is hoped that the present study will be helpful to the decision-makers while prioritising the actions to be taken for the water and soil conservation purposes. [ABSTRACT FROM AUTHOR]

The Indo-Ganga–Brahmaputra plain is one of the world's most vulnerable areas to catastrophic flood events. The severity and frequency of floods in this region have grown dramatically during the last few decades. As a result, an authentic map providing detailed spatial information on floods is required for reducing future flood disasters. This sort of map can be an invaluable tool for displaying flood-prone areas and determining the magnitude of flood risk. The Ramganga river (~ 200 km length) flowing in the western Gangetic plain was chosen for this investigation. This river is well known for its channel movement and flood hazards. The analytical hierarchy process model in GIS ambiance was used in this work to quantify and map flood risk in the lower Ramganga river basin. Six hazard-forming factors, namely relief, distance from active channel, slope, geomorphology, rainfall, and vegetation, and seven vulnerability factors, namely population density, household density, female density, road density, land use/land cover, female literacy, and total literacy, were chosen to determine flood risk zone (FRZ). According to the findings, about 35.29 per cent of the basin's total area is in a high to very high flood risk zone, where flooding occurs at one- to three-year recurring intervals. The flood risk map has been validated using ground truth data, and the Kappa index has been computed, indicating a high degree of accuracy for the FRZ thematic classes. [ABSTRACT FROM AUTHOR]

Landslides cause significant damage to property and life in the West Jaintia Hills district in Meghalaya, India, due to mountains, slopes, and extreme rainfall. Landslide susceptibility maps are highly efficient and helpful for disaster preparation and management. The determination of this work is to create a landslide susceptibility map (LSM) for West Jaintia Hills using the analytical hierarchy process (AHP) method. The previous landslide data are required for effective management of the current situation, and these data are divided into training samples (75%) and testing samples (25%) for predicting efficiency. The different landslide causative factors are investigated in this study: rainfall, slope, geomorphology, elevation, lineament density, LULC, distance from a road, NDWI, MSAVI, and NDVI. The causal factors are subdivided and weighted according to AHP, and a susceptibility map is prepared. According to the developed map, 19% of the region's total area is low susceptible, 52% moderate, 25% high, and 4% very highly susceptible. Field data and the receiver operating characteristics (ROC) approach are used to validate the produced map. The outcomes indicate that the predicted susceptibility map closely reflects previous and recent landslide events. The created map is accurate and can be applied to future land-use planning. [ABSTRACT FROM AUTHOR]

Drought depends upon many factors such as lack of precipitation, sunshine duration, and altitude. In this study, future rainfall trend pattern was studied, and an attempt was made to quantify the amount of rainfall deficit to cause drought occurrence and corresponding required water for the recovery of future drought. The non-standardized anomaly index (AI) was used to quantify the water deficit for future drought event, which is based on CMIP 6 climatic model generated rainfall data under the four different shared socioeconomic pathways' (SSPs) scenarios (SSP 1–2.6, SSP 2–4.5, SSP 3–7.0, and SSP 5–8.5) for the chosen study area (the upper Tapti River basin in central India). Our study found out that the future rainfall pattern would be an increasing trend for all the SSPs scenarios up to the end of the twenty-first century. Particularly, SSP 5–8.5 scenario projected increasing rainfall pattern and more number of drought events than other SSPs. In specific season, the future monsoon season showed more number of drought events. The water required for drought recovery was found increased from SSP 1–2.6 to SSP 5–8.5 scenario. The results presented in this study are helpful to adopt drought management activity in response to changing rainfall pattern under climate change aspect. [ABSTRACT FROM AUTHOR]

This paper delineates the groundwater potential zones (GWPZ) within the Tadri River Basin in the Western Ghats of India, using analytical hierarchical process (AHP) and geoinformatics-based techniques. Eight discrete parameters (slope, rainfall, drainage density, lineament density, lithological units, geomorphological units, soil types and land use, and land cover classes) were selected and weighted according to their influence on groundwater availability and combined in a hierarchical manner to obtain the resulting GWPZ map. This was classified into five classes (very good, good, moderate, low and very low) depending upon the relative groundwater availability within each. Excellent GWPZ were present in the lower part of the Tadri Basin and along the Western Ghats' foothills beside the coast while the pediment-pediplain complex had less viability. The GWPZ map was validated using the area's average groundwater depth and well density, via the receiver operating characteristic (ROC) curve and the area under curve (AUC), which elicited an accuracy of 79% and 82.1%, respectively. Sensitivity analysis revealed that the basin's lithology, land use/land cover, drainage density and lineament density had the most impact in GWPZ delineation. [ABSTRACT FROM AUTHOR]

A large fraction of the world's poor rely on rain-fed agriculture, which makes them vulnerable to changes in rainfall patterns. In this paper, we examine whether spatial correlation in rainfall results in these households also being vulnerable to an adverse spatial-spillover effect. In particular, we use household-level panel data from India along with high-resolution meteorological data to show how rural household consumption varies with own-region rainfall as well as rainfall in neighboring areas. We find that while greater own rainfall has a positive effect on rural household consumption, greater rainfall in neighboring regions has an adverse spatial-spillover effect. Our results suggest that when this spillover effect is taken into account, the positive impact of own-region rainfall on household consumption falls by 38 percent. [ABSTRACT FROM AUTHOR]

Poverty is not only the focal issue that has drawn worldwide attention but is also an essential issue in people's livelihoods. This research examines the primary factors of poverty in the Birbhum district. Multivariate statistical techniques have been used to identify the primary determinants. Ten parameters have been identified as significant drivers of poverty, six of which are physical, viz. slope, elevation, drainage density, pond frequency, soil texture, and rainfall. The remaining four sociocultural and economic parameters are literacy, major market center, population growth, and road density. A linear relationship has been established between the explanatory and response variables where the R-square or coefficient of determination value is 0.741, and this relationship explains more than 74% of the variables. The P-value of multi-linear regression is 0.000, which validates the model and permits the data for factor analysis to extract the major determinants. Factor analysis indicates that five essential factors have been found based on their eigenvalue viz., agro-climatic factor, infrastructural and educational factors, hydrological factor, demographic factor, and pedological factors. All the p-values of the correlation matrix are < 0.05, meaning all the relationships are valid and significant. This research also demonstrates the spatial analysis of data using GIS technology. The western part of the study area has been affected by the high influence of all factors due to the presence of plateau fringe and associated low productivity. The outcomes of the research are scientifically significant and this study helps the planners, higher authorities, and social workers to eradicate poverty from this region through formulating better policies and management. [ABSTRACT FROM AUTHOR]

Flood is always a source of social lamentation, huge infrastructural losses and disruption to economic activities in Bhagirathi Sub-basin in India. Climate variability and increasing flood incidents have created a dilemma for social, economic and environmental conditions of the affected communities. These implications necessitate assessing overall flood vulnerability to minimize their short and long-term impacts. This study presents a comprehensive analysis of composite vulnerability among the flood affected communities in Bhagirathi Sub-basin. Data for analyzing composite flood vulnerability were derived from an in-depth survey of 432 households selected through stratified random sampling method in the Sub-basin. Domains of vulnerability such as quality of life, social & economic status, health impacts, ecological implications, losses and adaptation were examined. A total of 95 indicators of these domains were considered to prepare composite vulnerability index of the selected villages. Relationship between vulnerability and households' characteristics was ascertained using cross tabulation and multinomial logistic regression. Analysis of composite vulnerability index (CVI) revealed very high vulnerability in Nutanhat, Bakkhali, Jhara, Gopalpur, Jayarampur, Titiha, Uchildaha and Mohanpur villages. High vulnerability was observed in Banagram, Mayapur, Amravati, Gobindapur, Raichak Boltala, Talim Nagar Minakhan and Majhirmana villages while Kalna Municipality was found under moderate vulnerability. High losses, ecological & health implications and low socioeconomic conditions of the households aggravated very high to moderate vulnerability in these villages. Gender, income and land possession were found strongly associated with high vulnerability while flood insurance, farming purposes and changes in rainfall pattern were identified inducing moderate vulnerability. CVI analysis assisted in identifying the priority villages for effective policy implications. The study calls for policy implications for lessening the impact of flood in the Sub-basin. [ABSTRACT FROM AUTHOR]

Although the ecological and economic services rendered by the wetland ecosystems are innumerable, the exposure of inhabitants to hazardous climatic events is on the rise. For instance, the Kuttanad wetland ecosystem in Kerala, India, faces uneven rainfall patterns, leading to recurrent flooding. The present study examines people's vulnerability to elevated flooding risk in the region, factors responsible for migration in the wake of climate change and their adaptive capacity to such events. The primary survey-based study follows the theoretical framework of vulnerability and adaptive capacity. Physical asset loss, sinking houses, elevated health risks and loss of livelihood are factors identified for increased vulnerability to flood risks. The exacerbating vulnerability translates into the mass migration of local inhabitants. The Probit regression underscores the role of households' socio-economic background in migrating from the region, seeking safe havens. Marginalised social groups and people reliant on the local environment are most vulnerable. As per the study, the absence of pre and post-flood measures affects the adaptive capacity of the inhabitants. Given the gravity of flooding risk, the study suggests channelised policy measures that are quintessential to improve their resilience and adaptive capacity. [ABSTRACT FROM AUTHOR]

Rainfall variability is a key element in water resource management. There has been significant variability in rainfall in Arunachal Pradesh, with the state being in the highest rainfall zone in India. The present study attempts to investigate the annual rainfall trend along with the change points in the rainfall time series, considering more than a hundred years of data (1901–2002) in eleven districts of Arunachal Pradesh. First, a serial correlation test was conducted on the entire time-series data for the three districts to detect the serially independent time series, followed by the non-parametric Mann–Kendall test and Spearman's rank correlation test to ascertain the presence of a statistically significant trend in hydrological climate variables. Sen's slope method was applied to estimate the magnitude of the trend. Pre-whitening, trend free pre-whitening Mann–Kendall, and bias corrected pre-whitening, along with two variance correction approaches, are applied to the serially correlated data. All the districts under consideration show a decreasing trend in rainfall in the investigation over the years. A comparative study of the trend test method was also carried out. Further, to identify the trend change points in the time series, a sequential Mann–Kendall test is conducted. Assessing 100 years of time series data for the region makes the study unique of its kind and is likely to play a vital role in environmental policymaking. Furthermore, it will be helpful for water resource management, land use, land cover management, sustainable agricultural planning, and the overall socio-economic development of the region. [ABSTRACT FROM AUTHOR]

Floods are the most frequent of all natural disasters that occur in India. Although one of the main reasons for these disasters is the variations in monsoon and heavy inflows during the four months of the rainy season, India's asymmetric geomorphic features play an important role in determining the nature and intensity of floods in different regions. The destruction due to floods can be monitored with Internet of Things (IoT) systems. Flood forecasting can be done to warn people and evacuate them to safer places. A real-time end-to-end system generates an early flood alert based on various hydro-meteorological parameters received from sensing devices. In this study, an IoT-based system senses the hydrological data such as water discharge and water level, as well as data related to climate like rainfall, humidity, temperature, wind direction and speed. The task of estimating the water discharge gets difficult since rivers have changing geographic properties. To resolve this issue, a novel technique is proposed to estimate river discharge based on the sectional area and flow of the river. Additionally, the challenges associated with quantifying the overall quantity of rainfall resulting from the unpredictable behaviour of the local meteorological conditions have been addressed. [ABSTRACT FROM AUTHOR]

Leptospirosis, a formidable zoonotic threat spawned by Leptospira, plagues tropical and subtropical realms. This study delves deep into tropical Indian states, namely, Kerala, Gujarat, Karnataka, Maharashtra, and Tamil Nadu, unraveling the dynamics of leptospirosis through a comprehensive mathematical model that embraces temperature-driven growth rates of Leptospira. Sensitivity analysis and parameter estimation techniques fortified the model's accuracy, unraveling the factors shaping leptospirosis transmission. Notably, the numerical results highlight the significant impact of rainfall, fishing, climate, mining, agriculture, and cattle farming on leptospirosis prevalence in the endemic states of India. Finally, our study urges resolute preventive action to control and combat leptospirosis in India. Strengthening surveillance, impactful awareness campaigns, targeted interventions, and improved hygiene practices among high-risk individuals are vital. Embracing these proactive strategies will alleviate the burden of leptospirosis and enhance public health in India and beyond. [ABSTRACT FROM AUTHOR]

This study documents the climatological feature (1951–1980) and recent changes (1981–2020) in rainfall characteristics considering the observed nearly full spectrum of rain event sizes (daily contiguous rain area (CRA) events) in all seasons over India. It is found that the low frequency very large CRA (~synoptic scale) from monsoon season contributes ~50% of annual rainfall. However, the small-sized CRA (isolated thunderstorms) are the most frequent daily rain events (~70% of annual frequency) and hence are important for rain-fed agricultural practices. The well-documented widespread drying trend in the monsoon season has manifested in the annual rainfall trend but with reduced magnitude illustrating the compensatory effect from other seasons. Spatial aggregated annual statistics show that there is no significant change in rainfall amount and frequency of occurrence of rain events in the recent past compared to the base period. However, seasonally the pre-monsoon rainfall amount has increased significantly. Annually, the number of extremely heavy CRA (EHR) events have significantly increased by ~55% owing to a significant increase in pre-monsoon and monsoon rainfall. In all seasons, small-sized extremely heavy CRA has intensified substantially by 50–200% as compared to the base period. Additionally, the rain events from areal category large (~Mesoscale Convective Complexes (MCC)) have intensified in all seasons except winter. Thus, to decrease the uncertainty in rain-fed agricultural practices and better prediction of EHR to develop effective climate change mitigation strategies; process studies beyond monsoon season and processes other than synoptic scales are also required. [ABSTRACT FROM AUTHOR]

During the summer monsoon season, the Indian region experiences prominent south-westerly winds that extend up to the Philippines. These south-westerly winds encounter the Western Ghats (WG) and Deccan Plateau (DP). Many studies have discussed the role of WG in modulating the monsoon flow, convection and rainfall over the Indian region. However, the orographic influence of south-west orography (DP and WG both) on large-scale monsoon circulation remains unexplored. An experimental global model simulation was conducted with removed DP and WG orography to investigate this. The results showed that modifying the orography can alter the wind flow across the Philippines, Western North Pacific, and Bay of Bengal, leading to mid-tropospheric heating changes due to the monsoon trough position over these regions. This change in the monsoon trough position results in the stationary Rossby wave. The stationary Rossby wave generated strengthened winds over the south Bay of Bengal (BoB), which merges with the Philippine anticyclone over the Western North Pacific. The dynamical and thermodynamical changes are observed near Western North Pacific. Consequently, there is an increase in rainfall over the Western North Pacific and a decrease in rainfall over India. This study also found that the change in the monsoon flow affects the low-pressure systems and tropical cyclones. This work highlights how the South-West Indian orography affects the monsoon flow and synoptic variability across India, the West North Pacific, and the Philippines anticyclone. Thus, understanding the monsoon flow over the orography will help understand the dynamics. [ABSTRACT FROM AUTHOR]

An iconic feature of the Indian Summer Monsoon Rainfall (ISMR), a longer than June–September rainy season over Northeast India (NEI), while a much shorter one over northwest India is expected to be altered by climate change but an objective definition of the length of the monsoon rainy season (LRS) over the NEI is lacking. Here, defining the LRS objectively over NEI, we show that the El Niño and Southern Oscillation (ENSO) is a primary driver of LRS, while rainfall during LRS is poorly correlated with the ENSO. In contrast to a significant decreasing trend of LRS and LRS-rainfall during the historical period, the projected LRS under the SSP5–8.5 scenario continues to decrease while the LRS-rainfall acquires a significant increasing trend over NEI. A significant increase in the impact of hydrological disasters is expected over NEI in the future due to the increasing intensity and frequency of extreme rain events within a shorter rainy season. [ABSTRACT FROM AUTHOR]

During the monsoon season, flooding is common in several parts of India, and urban areas are becoming more prone to flooding even during less intense rainfall events as a result of the encroachment of waterbodies and natural drainage channels, increased impervious areas, and subsequent decrease in infiltration capabilities. In the years 2000, 2008, 2016, 2019, 2020, and 2021, severe flood events in our study area, the Saroor Nagar urban watershed in Telangana state, caused human loss, economic devastation, and environmental devastation. Although flood risk cannot be completely eliminated, it can be significantly reduced by developing a flood hazard model to identify flood-prone areas in a watershed, which can assist decision makers seeking comprehensive flood risk management. The flood hazard map is created by integrating Geomatics with multi-criteria decision analysis and the analytical hierarchy process (AHP). Topographic wetness index (TWI), digital elevation model (DEM), slope, precipitation, drainage density, distance to waterbody, soil, and land use land cover (LULC) were the flooding causative elements considered in this study. The resulting flood risk map is divided into four distinct categories that reflect flood danger levels of low, moderate, high, and extremely high. The flood risk map revealed that the moderate risk zone decreased from 50.2 to 45.7% of the study area between 2008 and 2020, while the high-risk zone increased from 45.2 to 52.8%. The flood susceptibility map is validated using crowdsourcing techniques. The findings demonstrated that a Geographic Information System (GIS)-based multi-criteria analysis framework for flood hazard analysis could be effectively used to aid disaster management decision-making. [ABSTRACT FROM AUTHOR]

Mankind is currently facing a crucial challenge in terms of climate change which may result in severe drought in many areas across the world. The present study analysed the joint return periods of meteorological drought events for historical and future projections across the Vidarbha region of India. To describe the drought occurrences in the area, the study used the standardized precipitation evapotranspiration index. The properties of each drought event, viz., duration, severity, and peak, were identified to model the multivariate drought risks. Several copula families were evaluated using statistical tests for joint dependency modelling of drought properties. The obtained joint distribution and selected univariate marginals are further used for estimating trivariate return periods for the historical and future climate change scenarios. The drought properties exhibited good interdependency justifying the use of a trivariate framework. The observed historical data of rainfall, maximum and minimum temperature were collected from the India Meteorological Department. The latest future projections of climate variables from coupled model intercomparison project phase 6 (CMIP6) datasets were acquired from NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) for six general circulation models. The copula-based trivariate frequency analysis was performed to compare the return periods associated with 'moderate' and 'above moderate drought' (i.e., combination of severe and extreme drought) events for the reference period (1981–2020) and future periods (2021–2100) under two SSP scenarios, SSP2-4.5 and SSP5-8.5. The 'AND' condition return period showed high drought risk during historical period covering a larger area. The findings of the study suggested that for 'AND' condition the trivariate frequency of above moderate droughts would increase in the future across Vidarbha region. The analysis showed that during near future more area will be under the risk of above moderate drought conditions compared to the far future, and the high-risk zones will be more under SSP5-8.5 than SSP2-4.5 scenario. The frequency of moderate drought episodes will be higher than the above moderate drought events, but the percentage area in the high-risk zone will be lower. The 'OR' condition return period projected alleviation of drought risk in the future, while the far future projected more area under drought risk compared to near future for the SSP scenarios. [ABSTRACT FROM AUTHOR]

The Indian summer monsoon trough (MT) is a dynamically active region of the quasi-stationary feature extending from north-western India towards the Bay of Bengal (BoB). The eastern rim of the MT (EMT) is modulated by the embedded synoptic-scale monsoon low-pressure systems (LPSs) in north BoB. We examine the spatiotemporal variability of the convective storm (CS) in the EMT region, which lies along the pathway of LPSs. A Lagrangian-based objective-tracking method, Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN), was injected into Kolkata S-band radar observations of nine wet seasons (June–September, 2009–2017). CSs frequently occur near the onshore areas and is linked to the formation and propagation of LPSs in the BoB. By examining the relationship of CS top heights with reflectivity lapse rate and fractional volume of 40 dBZ or more, three vertical categories of CS have been identified as shallow (SCS) below 5 km, medium depth (MDCS), and deep CS (DCS). The land–ocean contrast in the spatial distribution of MDCS and DCS and the contoured frequency by altitude diagram shows continental convective vigor. Although short-lived smaller CSs account for most storms in EMT, the bulk of the precipitation (75 % ) contributed from the infrequent largest storms. A weak (strong) linkage between the precipitation flux and CS top (area-time integral) gives a clue on the importance of CS voluminous organization rather than the linkage of heaviest rainfall to tallest CS. While most CSs propagate inland following the mass-weighted mean winds, the largest CS propagate offshore steered by the mid-level wind at 700 hPa. [ABSTRACT FROM AUTHOR]

Rainfall-induced landslides on steep slopes are potential hazards to life and property. The state of Kerala in India is witnessing unprecedented death and damage since last 3 years due to landslides triggered by heavy rainfall. In this paper, we present a cause-impact analysis of landslides that occurred due to the monsoon rainfall in the years 2018, 2019 and 2020. We have created an event-based landslide inventory post-rainfall season for the entire state and then selected six major landslides for detailed analysis. Event-based landslide inventory maps are prepared from Earth observation satellite data such as Resourcesat-2, 2A, Cartosat-2S, WorldView-2, Pleiades-1A and 1B, SPOT-6 and 7 and Sentinel-2 acquired before and after the rainfall events. Then, rainfall data were analysed to find out the role of daily and antecedent rainfalls on landslide trigger. Finally, we have carried out numerical debris flow modeling to characterise landslides and assess the impact. Our results show that landslides are triggered by antecedent rainfall of variable durations and are mostly debris flow type that has followed the channel path. The runout length and lateral spreads are controlled by variable frictional parameters of the slope material. Furthermore, reactivation at the crown region is also noticed in few cases that need to be monitored. [ABSTRACT FROM AUTHOR]

Prediction of the seasonal monsoon rainfall over India relies largely on the well-known relationship with El Niño and Southern Oscillation (ENSO) and is possible because reasonably reliable seasonal predictions of ENSO are now available. Usually, the cold phase of ENSO is associated with above-normal monsoon rainfall and the warm phase of ENSO with below-normal rainfall. There are, however, exceptions: years in the cold phase of ENSO with below-normal monsoon rainfall and even drought conditions. We term these exceptional events 'rogue La Niñas'. Clearly, an explanation of these exceptional cases will improve the predictive skill. Here we show that for the part of the Arabian Sea, east of the upwelling region and north of the equatorial belt (60°–70°E, 10°–23°N), the correlation of outgoing longwave radiation with Indian summer monsoon rainfall is even higher than that with the equatorial central Pacific associated with ENSO. Convection over this region is triggered by ENSO, but is modulated by the underlying sea surface temperature (SST). There is a minimum of SST of about 28.1°C above which the convection over the Arabian Sea is high enough and there are no rogue La Niñas. Furthermore, we show that, in this region, the SST of June–September is related to the SST of April–May. When April–May SST is >29.6°C, June–September mean SST is always >28.1°C and there are no rogue La Niñas; the monsoon rainfall is always normal or above normal as expected with a La Niña. Thus the chance of a rogue La Niña can be predicted from the April–May SST of the Arabian Sea. [ABSTRACT FROM AUTHOR]

Floods are one of the natural disasters that occur most frequently in the Brahmani River of Eastern India. Frequent floods in the area, results from anthropogenic activities, climate change, unpredictable weather, and heavy rainfall cause loss of life, property, and resources. Flood hazard susceptibility maps will help in identifying risk-prone locations, which aids in flood management and the decision-making process. The main objective of this study is to evaluate the performance of bivariate statistical and hybrid models such as Frequency ratio, Weight of Evidence (WoE), Evidential Belief Function (EBF), Index of Entropy, Analytical Hierarchy Process (AHP), AHP–TOPSIS and AHP VIKOR to prepare flood susceptibility maps of the Brahmani River basin, India. The flood conditioning factor includes Slope, Aspect, Elevation, Curvature, Geology, Geomorphology, Topographic Wetness Index, Topographic Ruggedness Index, Distance from Streams, Distance from Roads, Rainfall, Normalized Difference Vegetation Index (NDVI), Stream Power Index, Soil, Drainage Density, and Land use Land cover. The flood inventory was prepared using various atlas and news reports. Multicollinearity among all the causative parameters is measured using the Variance Inflation factor (VIF) and Tolerance (TOL). The collection of a spatial database and flood inventory from various sources is used to prepare flood susceptibility maps. The flood susceptibility map is categorised into very high, high, moderate, low, and very low classes using the natural break method. The models are validated using flood training (70%) and testing (30%) points. The accuracy of flood susceptibility maps is determined using Area Under Receiver Operating Characteristics (AUROC) curves and Seed Cell Area Index (SCAI) values. The success rate of Frequency ratio (AUC 0.91), EBF (AUC 0.90) and WoE (AUC 0.86) show the highest accuracy among all other models in AUC. WoE, IoE, and FR gave reliable results in the SCAI validation process. The susceptibility maps will help in identifying hazard-prone areas, which will help policy makers better assessment of natural hazards. [ABSTRACT FROM AUTHOR]

Most extreme precipitation in the densely populated region of central India is produced by atmospheric vortices called monsoon lows and monsoon depressions. Here we use satellite and gauge-based precipitation estimates with atmospheric reanalyses to assess 40-year trends in the rain rates of these storms, which have remained unknown. We show that rain rates increased in the rainiest quadrant of monsoon depressions, southwest of the vortex center; precipitation decreased in eastern quadrants, yielding no clear trend in precipitation averaged over the entire storm diameter. In an atmospheric reanalysis, ascent increased in the region of amplifying precipitation, but we could not detect trends in the intensity of rotational winds around the storm center. These storm changes occurred in a background environment where humidity increased rapidly over land while warming was more muted. Monsoon lows, which we show produce less precipitation than depressions, exhibit weaker trends that are less statistically robust. [ABSTRACT FROM AUTHOR]

The Indian summer monsoon rainfall (ISMR) exhibits significant variability, affecting the food and water security of the densely populated Indian subcontinent. The two dominant spatial modes of ISMR variability are associated with the El Niño Southern Oscillation (ENSO) and the strength of the semi-permanent monsoon trough along with related variability in monsoon depressions, respectively. Although the robust teleconnection between ENSO and ISMR has been well established for several decades, the major drivers leading to the time-varying relationship between ENSO and ISMR patterns across different regions of the country are not well understood. Our analysis shows a consistent increase from a moderate to substantially strong teleconnection strength between ENSO and ISMR from 1901 to 1940. This strengthened relationship remained stable and strong between 1941 and 1980. However, in the recent period from 1981 to 2018 the teleconnection decreased consistently again to a moderate strength. We find that the ENSO–ISMR relationship exhibits distinct regional variability with time-varying relationship over the north, central, and south India. Specifically, the teleconnection displays an increasing relationship for north India, a decreasing relationship for central India and a consistent relationship for south India. Warm SST anomalies over the eastern Pacific Ocean correspond to an overall decrease in the ISMR, while warm SST anomalies over the Indian Ocean corresponds to a decrease in rainfall over the north and increase over the south of India. The central Indian region experienced the most substantial variation in the ENSO–ISMR relationship. This variation corresponds to the variability of the monsoon trough and depressions, strongly influenced by the Pacific Decadal Oscillation and North Atlantic Oscillation, which regulate the relative dominance of the two spatial modes of ISMR. By applying the PCA-Biplot technique, our study highlights the significant impacts of various climate drivers on the two dominant spatial modes of ISMR which account for the evolving nature of the ENSO–ISMR relationship. [ABSTRACT FROM AUTHOR]

Clouds and aerosols contribute substantial uncertainty to the estimation of precipitation patterns at the global and regional scale. The present study focuses on understanding spatiotemporal and vertical variability in aerosol–cloud interactions over western India and the Arabian Sea. The study was conducted for the period 2000–2018 using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) satellites and a regional climate model. In situ measurements of rainfall for western India from the India Meteorological Department (IMD) were also used. High values of aerosol optical depth (0.21–1.10) were observed for deficit rain, whereas low values (0.15–0.85) accounted for excess rain over western India. Over both regions, a negative linear correlation between aerosol optical depth and cloud effective radius and between aerosol optical depth and precipitation rate for excess, normal, and deficit rainfall conditions, with variations in correlation coefficient and slope values for the three monsoon conditions. In general, higher slope values were observed for excess rain over both regions, signifying a greater rate of change in cloud effective radius and precipitation rate with change in aerosol optical depth. Simulated cloud parameters including fractional cloud cover and the mass fraction of cloud liquid water content showed significant vertical variability for different monsoon conditions along different latitudes and longitudes for western India and the Arabian Sea, respectively. [ABSTRACT FROM AUTHOR]

This study examines the sensitivity of regional climate model version 4.7 (RegCM4.7) to the choice of the land surface scheme (LSS) and model domain in simulating the Indian summer monsoon rainfall (ISMR). The RegCM4.7 shows improved performance in simulating the ISMR on using the Community Land Model version 4.5 (CLM) LSS as compared to the Biosphere–Atmosphere Transfer Scheme (BATS) over the most part of India. However, the (CLM) LSS as compared to the BATS over most parts of India. However, the value addition of using CLM over BATS varies spatially and also the choice of domain. For example, a more significant improvement in simulating mean precipitation is seen over the eastern (western) flank of central India on using the larger Coordinated Regional Climate Downscaling Experiment (CORDEX-SA) domain, while western and southern India shows a larger improvement in the Indian (IND) model domain. The model performance greatly improves on using the smaller Indian (IND) model domain as compared to the larger CORDEX-SA domain for the precipitation intensity greater than 15 mm/day. The best model performance in simulating the ISMR and its associated features is obtained when CLM LSS is used in combination with the IND domain (e.g., CLM IND model setup). It is found that the impact of the model domain is higher as compared to the LSS towards improved ISMR simulation. The smaller model domain size helps in the better simulation of the convective precipitation. The CORDEX-SA domain produces imperfect low-level jets (shifted westwards), which tend to reduce the moisture supply towards the Indian land region, thus leading to dry rainfall bias over the central Indian region. The BATS scheme also performs poorly in this regard as compared to the CLM scheme. The inclusion of CLM scheme in a model setup that uses the IND domain largely overcomes the problem of dry bias over the Indian region. [ABSTRACT FROM AUTHOR]

Northern Hemispheric summer monsoons were more intense during the mid-Holocene (MH ~ 6000 years ago) and coincided with a northward shift of the Intertropical Convergence Zone (ITCZ) compared to the pre-industrial (PI) climate. Ancient civilizations in the Indus valley, Mesopotamia, and Egypt appear to have flourished during this period, thanks to abundant water availability. This study exploits a high-resolution variable grid global atmosphere model to understand the role of orbital forcing and ocean surface conditions in strengthening the monsoons and shifting the ITCZ northward over Africa, India, and East Asia during the MH. The combined impact of orbital forcing and sea surface temperature (SST) boundary conditions led to a change in monsoon rainfall of around 42, 30, 21, and 41% over Africa, East Asia, India, and northwest India (NWI) relative to the PI conditions. Changes in orbital parameters alone account for more than 36 and 26% of total rainfall increases in Africa and East Asia. Over the Indian subcontinent, the strengthening of monsoon was primarily a combined effect of SST and orbital forcing. In contrast, the SST boundary condition alone could explain the 39% of rainfall increase over NWI, where the Indus valley civilization once existed. Through moisture budget analysis, the study further illustrates the role of dynamic and thermodynamic factors responsible for the changes in monsoon precipitation. The enhanced monsoon resulted in a northward shift of ITCZ by around 3°N, 1.9°N, and 2.5°N over Africa, East Asia, and India, respectively, compared to its PI position. Analogous to the precipitation changes, orbital forcing mostly mediated ITCZ changes across Africa and East Asia, but the combined impact of orbital forcing and SST was responsible for the changes over India. [ABSTRACT FROM AUTHOR]

The present study addresses the application of weight of evidence (WoE) model (probabilistic approach) to identify the spatial distribution of landslide susceptibility (LS) in Darjeeling Himalaya, Eastern India. Fifteen triggering factors of landslide such as elevation, geology, slope angle, slope curvature, soil, slope aspect, drainage density, lineament density, distance to drainage, distance to lineament, topographic wetness index (TWI), normalized differential vegetation index (NDVI), stream power index (SPI), land use land cover (LULC) and rainfall were considered as spatial database to perform WoE model using remote sensing (RS) and geographic information system (GIS) and to develop landslide susceptibility zonation (LSZ) map. The relative importance of weights of each factor was calculated using the WoE method. The LSZ map depicted that very low (VL), low (L), moderate (M), high (H) and very high (VH) LS zones cover an area of 3.33%, 8.46%, 29.94%, 38.07% and 20.20% respectively. Two thousand seventy-nine landslide locations have been identified and considered to perform the accuracy of the study. The receiver operating characteristics (ROC) curve resulted in 78.90% prediction accuracy with and area ratio of 0.7890 which recognised WoE model as a significant statistical model for LS mapping for Darjeeling Himalaya. Very low to very high LS zones registered with the frequency ratio (FR) of 0.00, 0.21, 0.36, 0.85 and 2.73 respectively, which is positively correlated to landslides. The outcome of the study will be very helpful to the planners and policy makers for implementing effective measures to mitigate landslides hazards in Darjeeling Himalaya. [ABSTRACT FROM AUTHOR]

This paper presents the lightning activity, aerosol optical depth (AOD) and climatic parameters (Bowen ratio, relative humidity, rainfall, maximum surface temperature and maximum updraft speed) over wet (Northeast India—NEI) and dry (Northwest India—NWI) land surfaces in a comparative analysis. The analyses are performed on flash counts and weather data of 17 years (1998–2014) retrieved from the Tropical Rainfall Measuring Mission (TRMM) lightning imaging sensors (LIS) and the Moderate Resolution Imaging Spectroradiometer (MODIS), respectively. The first higher peak for flash counts during pre-monsoon (April–May) and second lower peak during September over wet NEI and dry NWI regions indicate the development of strong electrified storms during pre-monsoon and weakly electrified clouds during the withdrawal phase of the southwest summer monsoon. The monthly means of flash counts, Bowen ratio, maximum surface temperature, AOD and maximum updraft speed are higher by 27, 81, 16, 50 and 16%, respectively, for NWI than those of NEI. The relative humidity and rainfall are higher by 12% and 73% for NEI than those of NWI, respectively. The Pearson's correlation coefficients of lightning activity with climatic parameters and AOD demonstrate the distinctive orographic lifting, moisture content and vertical wind shear in the upper troposphere in the regional climatic zone of dry NWI in comparison to that of wet NEI. [ABSTRACT FROM AUTHOR]

Prediction of long-term rainfall patterns is a highly challenging task in the hydrological field due to random nature of rainfall events. The contribution of monthly rainfall is important in agriculture and hydrological tasks. This paper proposes two data-driven models, namely biogeography-based extreme learning machine (BBO-ELM) and deep neural network (DNN), to predict one, two, and three month-ahead rainfall over India (All-India and six other homogeneous regions). Three other data-driven models called ELM, genetic algorithm (GA)-based ELM, and particle swarm optimization (PSO)-based ELM are used to compare the performance of the proposed models. Firstly, partial autocorrelation function (PACF) is applied in all datasets to select the optimal number of lags for input to the models. Secondly, the wavelet-based data pre-processing technique is applied in selected optimal lags and feed to the proposed models for achieving higher prediction performance. To investigate the performance of proposed models, a non-parametric statistical test, Anderson–Darling' Normality test, is performed in all India dataset. The wavelet-based proposed hybrid models show better prediction capability compared to optimal lag-based proposed models. This study shows the successful application of time-series data using proposed techniques (optimal lags-based BBO-ELM and wavelet-based DNN) in the hydrological field which may be used for risk mitigation from dreadful natural events. [ABSTRACT FROM AUTHOR]

Extreme rainfall events are becoming more frequent in South Peninsular India (SPI), which is resulting in an increase in flash floods, landslides, and damage to agriculture and infrastructure. However, because of the scarcity of rainfall data over remote areas and oceans, the reanalysis datasets are a boon for understanding various meteorological phenomenon. India's first high-resolution reanalysis dataset, Indian Monsoon Data Assimilation and Analysis (IMDAA), simulates past climate data at the regional or local levels. In this study, a comprehensive evaluation of IMDAA is carried out with respect to Indian Meteorological Department (IMD) daily gridded dataset over SPI during 2000–2020. It was found that monsoon and post-monsoon seasons demonstrated strong compatibility whereas annual and pre-monsoon seasons displayed some dissimilarity as depicted by Mahalanobis metric. Spatiotemporal analysis of IMDAA in capturing seasonal and annual climatic variations implied that the reanalysis product considerably showed a similar pattern to that of IMD neglecting some overestimations. According to the study, an analogy of 96% can be seen between IMDAA and IMD on an average scale. Results also suggest the efficiency of IMDAA model in capturing some extreme rainfall episodes better than the IMD. Consequently, the findings provide insight into the reanalysis product's ability to depict climatic variability and reliability in employing precipitation data estimated by IMDAA in modelling extreme events over SPI. [ABSTRACT FROM AUTHOR]

The largest reservoir of drinkable water on Earth is surface water. It is crucial for maintaining ecosystems and enabling people to adapt to diverse climate changes. Despite surface freshwater is essential for life, the current research shows a striking lack of understanding in its spatial and temporal dynamics of variations in outflow and storage across a sizable country: India. Numerous restrictions apply to current research, including the use of insufficient machine learning techniques and limited data series. This work uses cutting-edge and SOTA-method to use the available data and machine learning to accurately understand spatial and temporal dynamics of variations in surface freshwater outflow and storage using extended data series. The authors did the examination of thematic maps produced using ArcMap 10.8 from June'2005 to June'2020 using JRC dataset to track changes in the intensity of surface water. Google Earth Engine in Python API has been devised to detect changes in surface water levels and quantifying shifting map trends. Raster image viewing, editing, and calculation are done with ArcMap. For determining the relationship between declines in Surface water levels, changes in rainfall intensity and land surface temperature, variables were averaged over 13 rivers for 15 years. The change in surface water is reliant on independent variables of change in land surface temperature and rainfall intensity. The authors use the correlation between these parameters to achieve an average R-squared adjusted value of 0.402. The study's findings contribute to a better understanding of the matter and can be used across the world. [ABSTRACT FROM AUTHOR]