Increasing trend of extreme rain events over India in a warming environment

Assessing the accuracy and reliability of satellite-derived precipitation products in the Kosi River basin (India)

The present research endeavors to examine the effectiveness of four gridded precipitation datasets, namely Integrated Multi-satellite Retrievals for GPM (IMERG), Tropical Precipitation Measuring Mission (TRMM), Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), with the observed rainfall data of eight rain gauge stations of India Meteorological Department (IMD) from 2001 to 2019 in Kosi River basin, India. Various statistical metrics, contingency tests, trend analysis, and rainfall anomaly index were utilized at daily, monthly, seasonal, and annual time scales. The categorical metrics namely probability of detection (POD) and false alarm ratio (FAR) indicate that MERRA-2 and IMERG datasets have the highest level of concurrence with the observed daily data. Statistical analysis of gridded datasets with observed dataset of IMD showed that the performance of the IMERG dataset is better than MERRA-2, PERSIANN, and TRMM datasets with "very good" coefficient of determination (R2) and Nash-Sutcliffe Efficiency values for monthly data. Trend analysis of gridded seasonal data of IMERG showed similar trends of observed seasonal data whereas other dataset differs. IMERG also performed well in identifying wet and dry years based on annual data. Discrepancies of the satellite sensor in capturing the precipitation have also been discussed. Thus, the IMERG dataset can be effectively used for hydro-meteorological and climatological investigations in cases of lack of observed datasets.

Temporal and spatial aggregation of rainfall extremes over India under anthropogenic warming

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.

Climate change effects on aquaculture production and its sustainable management through climate-resilient adaptation strategies: a review

Aquaculture witnessed a remarkable growth as one of the fastest-expanding sector in the food production industry; however, it faces serious threat from the unavoidable impacts of climate change. Understanding this threat, the present review explores the consequences of climate change on aquaculture production and provides need based strategies for its sustainable management, with a particular emphasis on climate-resilient approaches. The study examines the multi-dimensional impacts of climate change on aquaculture which includes the shifts in water temperature, sea-level rise, ocean acidification, harmful algal blooms, extreme weather events, and alterations in ecological dynamics. The review subsequently investigates innovative scientific interventions and climate-resilient aquaculture strategies aimed at strengthening the adaptive capacity of aquaculture practices. Some widely established solutions include selective breeding, species diversification, incorporation of ecosystem-based management practices, and the implementation of sustainable and advanced aquaculture systems (aquaponics and recirculating aquaculture systems (RAS). These strategies work towards fortifying aquaculture systems against climate-induced disturbances, thereby mitigating risks and ensuring sustained production. This review provides a detailed insight to the ongoing discourse on climate-resilient aquaculture, emphasizing an immediate need for prudent measures to secure the future sustainability of fish food production sector.

Amplified drying in South Asian summer monsoon precipitation due to anthropogenic sulfate aerosols

A declining trend in Indian summer monsoon precipitation (ISMP) in the latter half of the 20th century is a scientifically challenging and societally relevant research issue. Heavy aerosol loading over India is one of the key factors in modulating the ISMP. Using the state-of-the-state-of-the-art chemistry-climate model, ECHAM6-HAMMOZ, the impacts of South Asian anthropogenic sulfate aerosols on the Indian summer monsoon precipitation were investigated against: (1) 2010 La Niña (excess monsoon), (2) 2015 El Niño (deficit monsoon) in comparison to (3) normal monsoon 2016. Sensitivity simulations were designed with 48% enhancement in South Asian SO2 emissions based on a trend estimated from Ozone Monitoring Instrument (OMI) satellite observations during 2006-2017. The model simulations showed that sulfate aerosols reduce ISMP by 27.5%-43.3 %, while simulations without sulfate loading enhanced ISMP by 23% in 2010 La Niña and reduction by 35% in 2015 El Niño. This paper reports that sulfate aerosols loading over India reduce precipitation by aerosol-induced direct and indirect effects by inducing atmospheric cooling, weakening in the convection, and reduction in moisture transport to Indian landmass. This paper emphasizes the necessity of alternate use of energy to reduce sulfate aerosol emissions to solve water issues in South Asia.

Decoupling the effects of air temperature change on soil erosion in Northeast China

Changes in the air temperature tend to indirectly affect soil erosion by influencing rainfall, vegetation growth, economic development, and agricultural activities. In this study, the partial least squares-structural equation model (PLS-SEM) was used to decouple the impacts of temperature change on soil erosion in Northeast China from 2001 to 2019, and the indirect effect of temperature change on the pathways of natural and socioeconomic factors was analyzed. The results showed that temperature increase in Northeast China caused an increase in soil erosion by increasing rainfall and promoting economic development. Under the pathway of natural factors, in spring, the promoting effect on soil erosion under the influence of temperature change on rainfall was greater than the inhibiting effect on soil erosion under by the influence of temperature change on vegetation. In summer, the opposite effect was observed. Under the pathway of natural factors, over time, the promoting effect of temperature increase on soil erosion increased by 22.7%. Under the pathway of socioeconomic factors, temperature change not only aggravated soil erosion by promoting economic development, but also indirectly increased investments in agriculture and water conservation by improving the economy, thus inhibiting soil erosion to a certain extent. Over time, the contribution of temperature change to soil erosion through socioeconomic pathway was reduced by 44.4%. When the pathway of natural factors is compared with that of socioeconomics factors, temperature change imposed a more notable effect on the change in soil erosion through the socioeconomic pathway, indicating that human activities are the driving factors with a greater effect on soil erosion. Based on this, reasonable human intervention is an important means to alleviate soil erosion aggravation caused by rising temperatures.

Role of local absorbing aerosols in modulating Indian summer monsoon rainfall

Absorbing aerosols and their impact on the Indian monsoon system is highly complex and demands more scientific understanding. Our study using a chemistry-coupled regional climate model (RegCM 4.5) with idealized experiments observed that natural and anthropogenic absorbing aerosols (i.e., dust and carbonaceous aerosols) reduce monsoon precipitation in a seasonal time scale. More than 1 mm day-1 decline in mean summertime rainfall was observed over parts of the central Indian region and Indo-Gangetic plane for dust aerosol. A substantial reduction in the land-sea pressure gradient and lower tropospheric moisture distribution were found to control the observed modulation in rainfall. Near-surface wind circulation responded distinctly to natural (dust) and anthropogenic (carbonaceous) aerosols. The dust forcing weakened the monsoon trough by creating an anomalous anticyclonic circulation. The Northern Arabian Sea acted as a moisture source for the carbonaceous aerosol forcing. Intraseasonal rainfall over central India appeared to have a sharp reduction for dust forcing during early June, with a moderate increase for carbonaceous aerosols. Such quantification is essential for understanding the impact of aerosol forcing on regional climate change and the water cycle and has implications for emissions management and mitigation policies.

Baseflow significantly contributes to river floods in Peninsular India

Extreme rainfall prior to a flood event is often a necessary condition for its occurrence; however, rainfall alone is not always an indicator of flood severity. Antecedent wetness condition of a catchment is another important factor which strongly influences the flood magnitudes. The key role of soil moisture in driving floods is widely recognized; however, antecedent conditions of deeper saturated zone may contribute to river floods. Here, we assess how closely the flood magnitudes are associated to extreme rainfall, soil moisture and baseflow in 70 catchments of Peninsular India for the period 1979-2018. Annual flood magnitudes have declined across most of the catchments. Effect of flow regulations is also assessed to understand the impact of human interventions on flood characteristics. Reservoir regulation has positive effect by reducing the flood peak and volume, whereas the duration of flood events has increased after the construction of dams. Baseflow exhibits similar patterns of trends as floods, whereas trends in rainfall and soil moisture extremes are weakly correlated with trends in flood magnitudes. Baseflow is found to be more strongly influencing the flood magnitudes than soil moisture at various time lags. Further analysis with event coincidence analysis confirms that baseflow has stronger triggering effect on river floods in Peninsular India.

An increase in widespread extreme precipitation events during the northeast monsoon season over south peninsular India

While the spatio-temporal characteristics of Indian summer monsoon precipitation and its extreme spells have been extensively studied, the northeast monsoon, which occurs from October to December (i.e., post-monsoon season) and affects the southern peninsula of India, has not received as much attention. In light of this, the present study explores the spatio-temporal characteristics of precipitation during the northeast monsoon, with a particular emphasis on widespread extreme precipitation events and their associated large-scale synoptic systems, using recent ensemble of high-resolution regional climate models (RCMs) simulations and the Indian monsoon data assimilation and analysis (IMDAA) reanalysis. The study reveals that both models tend to underestimate the intensity and frequency of observed precipitation events, although their skills in reproducing the observed spatial patterns of both mean and extreme precipitation are quite high (r > 0.75). A substantial increase in widespread extreme precipitation events (nearly twofold), along with a 30% rise in precipitation intensity, has been observed in the recent decade compared to the 1980s, and models demonstrate a similar directional change but tend to underestimate the magnitude of observed precipitation. This increase appears to be linked to the rapid warming of the Indian Ocean, which, in turn, increases the water vapor in the atmosphere, ultimately supplying more moisture to the southeastern peninsular India. On the other hand, observed discrepancies in replicating some of the reported widespread impactful extreme precipitation events in the years 2007 and 2015 over the southern India region underscore the need for caution when interpreting model simulations. Low-pressure systems, such as troughs, associated with cyclonic circulations originating from the Bay of Bengal, have been identified as the primary sources of moisture fueling heavy precipitation during these events. Cluster analysis highlights varying synoptic patterns within the general framework, emphasizing the need for a more nuanced approach in simulating and forecasting extreme precipitation events. Overall, this study underscores the importance of enhancing modeling capabilities to better understand and prepare for the growing challenges posed by extreme precipitation events.

Mapping flood vulnerability using an analytical hierarchy process (AHP) in the Metropolis of Mumbai

The burgeoning significance of urban floods in the context of evolving climate dynamics and shifting rainfall patterns underscores the exigency for comprehensive investigation and mitigation strategies. The study employs a multi-criteria assessment (MCE) approach and the analytical hierarchy process (AHP) to evaluate flood-vulnerable zones, wards, and sub-category-wise flood locations in Greater Mumbai. The AHP technique is used to evaluate flood-vulnerable impacting parameters such as rainfall (29.42%), slope (20.96%), land use/land cover (17.52%), vicinity to sewers and storm-water drainage (13.99%), vicinity to natural drainage (8.97%), vegetation (5.58%), and soil (3.56%). The study area is classified under different vulnerable categories as severe vulnerable (46.72%), high to very high (18.74%), and slight to moderate (34.54%). Researchers analysed 234 waterlogged locations, revealing that 85.46% (200 locations) were in the severe to very high vulnerability category, and only 14.52% (34 locations) were in the other three categories. Flood locations are more affected by slope (under the categories of < 5 m and 5.01-10 m), built-up land, sewers and storm water drainage (< 125 m), natural drainage (< 250 m), rainfall (< 2000 to 2200 mm), lowest dense vegetation, and coastal alluvium in soils. These model-based flood vulnerability maps are crucial for planning flood conservation and mitigation measures.

Paradoxical behaviour of rainfall and temperature over ecologically sensitive areas along the Western Ghats

Initial reports signify some specific isolated locations in different latitudes, revealing a paradoxical increase in both heavy and very heavy rainfall events and also an increment in total, i.e., in both rainfall and temperature, over ecologically sensitive areas along the Western Ghats (WG). This paper presents a coherent study of the full-scale of daily rainfall and temperature over 27 well-spaced stations in the study area to determine its extent and investigate whether or not this contradictory behaviour is real. Also, an attempt has been made to assess the differential behaviour of rainfall, temperature, and heavy rainfall events in association with land use and land cover change (LULC). The analysis revealed that rainfall and temperature over the study area are increasing, whereas heavy rainfall events have increased during 1981-2020 with strong peaks after 2000 around 18-19°N (Mumbai metropolitan region), 14-16°N (mining and quarrying regions in Goa), and 9-12°N (a narrow strip of land spanning across the coastal towns of Karnataka and Kerala) latitudes. The majority of the rainfall excess years coincided with El Nino years, indicating that El Nino does not affect rainfall negatively. However, rainfall over the WG is influenced by local relief and cascading topography. The spatial pattern of average annual rainfall shows a decreasing trend from south to north because the elevation and span of rainfall occurrence are higher in the southern part of WG. The findings of the current research will help in building a strategy to address trends and patterns of climatic variables in association with LULC.

Spatio-temporal compounding of connected extreme events: Projection and hotspot identification

In general, the impact of two different connected extreme events is noticed on the same duration and spatial area. However, the connected extreme events can have footprint over different temporal and spatial scales. Thus, this article analyses the connected extreme events over India using the spatio-temporal compounding technique to understand the impact at different temporal and spatial scales. This approach is applied to analyse the historical and future connected extreme events. In the present study, coincident heat waves and droughts (Event C1), coincident heat waves and extreme precipitation (Event C2) are considered as connected extreme events. The future events are investigated using the suitable global climate models (GCMs) projections under three climate change scenarios (Shared Socioeconomic Pathways (SSP) 2-4.5, SSP3-7.0, and SSP5-8.5). The suitable GCMs are identified with the help of compromise programming. Subsequently, the hotspot regions are identified applying the Regional Climate Change Index (RCCI) method. The outcomes from the study suggest that with increasing temporal compounding, the mean duration of extreme events also increases. Highest increase in mean duration is observed for Event C1 over PI (Peninsular India), WCI (West Central India), and some parts of CNI (Central Northeast India) regions. The regions with high magnitude of duration have low magnitude of occurrence. The duration of Event C1 is likely to increase with respect to climate change scenarios and temporal compounding, especially in the PI region and some parts of WCI. However, there is insignificant change in the duration of Event C2. The PI region identified as the most vulnerable region followed by WCI and HR regions. The highest percentage of area under the emerging hotspot category is noticed under SSP5-8.5 climate change scenario.

Spatiotemporal Rainfall Variability and Trend Analysis of Shimsha River Basin, India

Karnataka state has the second highest rainfed agricultural land in India, where agricultural output relies heavily on rainfall. The Shimsha basin, a sub-basin of Cauvery in the state, comes under a semi-arid region and predominantly consists of rainfed agricultural land. Rainfall patterns have changed dramatically with time resulting in frequent floods and droughts. Understanding the spatiotemporal distribution of rainfall and its change patterns in the area would benefit sustainable agriculture planning and water resources management practices. The current study aims to determine the variability and trend in rainfall. The daily rainfall data of the Shimsha basin from 1989 to 2018 is collected, and the annual, seasonal, and monthly rainfall totals and the number of rainy days are derived. All the time series are subjected to statistical methods to examine rainfall variability and trend. The mean, standard deviation, coefficient of variation (CV), and Standardized Anomaly Index are used for the preliminary and variability analysis, while the coefficient of skewness and kurtosis are used to understand the rainfall distribution characteristics. The homogenous and serially independent series are identified by homogeneity and serial correlation tests. The trend in the homogenous and serially independent series is identified by Mann-Kendall and Spearman's rank correlation tests, while the magnitude of the trend is quantified using the Sen's slope technique, and the trend change point is evaluated using the sequential Mann-Kendall test. Based on the study, the average rainfall in the study area is 801.86 mm, with CV ranging from 43.3 to 22.27%. The southwest monsoon (SWM) season brings the greatest rain to the basin, followed by the post-monsoon (PM), summer, and winter seasons. In the annual time frame, except one station, all other stations have shown significant or insignificant increasing trends. The seasonal rainfall has shown insignificant rising trends during the summer and winter seasons while insignificant increasing and decreasing trends during the PM season. The SWM season has indicated significant increasing trends, insignificant increasing and decreasing trends. Overall, the study area has noticed an increased annual and seasonal rainfall except for the post-monsoon season, during which the rainfall showed a considerable decline. The findings of the study are helpful in water resource management, agricultural planning, and socioeconomic development in the study area.

River interlinking alters land-atmosphere feedback and changes the Indian summer monsoon

Massive river interlinking projects are proposed to offset observed increasing droughts and floods in India, the most populated country in the world. These projects involve water transfer from surplus to deficit river basins through reservoirs and canals without an in-depth understanding of the hydro-meteorological consequences. Here, we use causal delineation techniques, a coupled regional climate model, and multiple reanalysis datasets, and show that land-atmosphere feedbacks generate causal pathways between river basins in India. We further find that increased irrigation from the transferred water reduces mean rainfall in September by up to 12% in already water-stressed regions of India. We observe more drying in La Niña years compared to El Niño years. Reduced September precipitation can dry rivers post-monsoon, augmenting water stress across the country and rendering interlinking dysfunctional. Our findings highlight the need for model-guided impact assessment studies of large-scale hydrological projects across the globe.

Variation characteristics of extreme climate events in Southwest China from 1961 to 2017

Climate change is increasing the intensity of extreme climate events. Significant impacts of extreme climate events on human society and ecosystem have occurred in many places of the world, for example, Southwest China (SWC). In this study, the daily temperature and precipitation data from 438 meteorological stations are used to analyze the variation characteristics of extreme climate events in the SWC from 1961 to 2017. The annual extreme warm events show a significant increasing trend at 99% confidence level at most stations, and a few stations with a decreasing trend are mainly located in the southern Sichuan Province, the northern Yunnan Province and the western Guizhou Province. Meanwhile, the annual extreme cold events show a significant decreasing trend at 99% confidence level at most stations, and a few stations with an increasing trend are mainly distributed in the Sichuan Basin. Both the annual extreme heavy precipitation indexes and rainstorm indexes show nonsignificant increasing trends, but they differ greatly in the spatial distribution. These indexes in the western Tibet, Chongqing and most parts of Guizhou show significant increasing trends at 95% confidence level, while those in the central Sichuan and southeastern Yunnan show significant decreasing trends. The percentage of extreme heavy precipitation shows a significant increasing trend at 99% confidence level, especially in the northeastern Sichuan, the central-eastern Guizhou and the central Yunnan. Overall, under the background of global warming, the extreme warm events in SWC increase significantly from 1961 to 2017, and the extreme cold events decrease significantly. The variation trends of extreme precipitation events differ greatly in different regions, and the percentage of extreme heavy precipitation increases significantly.

Manifestation of spatially varying demarcations in Indian rainfall trends through change-point analysis (1901-2020)

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.

Spatiotemporal variability of extreme temperature indices and their implications over the heterogeneous river basin, India

The current study on spatiotemporal variability of temperature presents a holistic approach for quantifying the joint space-time variability of extreme temperature indices over the physio-climatically heterogeneous Tapi River basin (TRB) using two unsupervised machine learning algorithms, i.e., principal component analysis (PCA) and cluster analysis. The long-term variability in extreme temperature indices, recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI), was evaluated for 1951-2016. The magnitude and statistical significance of the temporal trend in extreme temperature indices were estimated using non-parametric Sen's slope estimator and modified Mann Kendall (MMK) tests, respectively. The multivariate assessment of temporal trends using PCA resulted in four principal components (PCs) encapsulating more than 90% variability. The cluster analysis of corresponding PCs resulted in two spatial clusters exhibiting homogeneous spatiotemporal variability. Cluster 1 is characterized by significantly increasing hottest, very hot, and extremely hot days with rising average maximum temperature and intraday temperature variability. On the other hand, cluster 2 showed significantly rising coldest nights, mean minimum, mean temperature, and Tx37 with significantly decreasing intraday and interannual temperature variability, very cold, and extremely cold nights with reducing cold spell durations. The summertime heat stress computation revealed that the Purna sub-catchment of the Tapi basin is more vulnerable to various health issues and decreased work performance (> 10%) for more than 45 days per year. The current study dealing with the associated effects of rising temperature variability on crop yield, human health, and work performance would help policymakers formulate better planning and management strategies to safeguard society and the environment.

Fingerprinting of rainfall over semi-arid region, Western India, using MATLAB and GIS

The present study investigates long-term changes in the rainfall regime over the Sabarmati River Basin, Western India, during 1981-2020 using computational and spatial analysis tools. Daily gridded rainfall data from India Meteorological Department (IMD) at 0.25 × 0.25 spatial resolution was employed to determine changes in rainfall at annual, monthly, and seasonal scales and analyze changes in rainfall characteristics using different thresholds for dry/ wet days and prolonged spells over Western India. Mann-Kendall test, Sen slope estimation, and linear regression analysis indicate that annual and monsoon rainfall over the basin has increased while the rest of the seasons have shown a declining trend. However, none of the trends obtained was found to be statistically significant. Spatial analysis of rainfall trends for each decade between 1980 and 2020 revealed that certain parts of the basin had experienced a significant declining trend during 1991-2000. Monthly rainfall analysis indicates the presence of a unimodal distribution of rainfall and a shift in rainfall towards later monsoon months (August and September). It is also inferred that days with moderate rainfall have decreased while low and extreme rainfall events have increased over the basin. It is evident from the study that the rainfall regime is highly erratic, and the study is important in understanding the changes in the rainfall regime during the last 40 years. The study has significant implications for water resource management, agricultural planning, and mitigation of water-related disasters.

Quantifying the role of antecedent Southwestern Indian Ocean capacitance on the summer monsoon rainfall variability over homogeneous regions of India

The role of ocean variability is at a focal point in improving the weather and climate forecasts at different spatial and temporal scales. We study the effect of antecedent southwestern Indian Ocean mean sea level anomaly (MSLA) and sea surface temperature anomalies (SSTA) as a proxy to upper ocean heat capacitance on all India summer monsoon rainfall (AISMR) during 1993-2019. SSTA and MSLA over the southwestern Indian Ocean (SWIO) have been influenced by El Niño-Southern Oscillation (ENSO), the impact of ENSO-induced SWIO variability was low on rainfall variability over several homogeneous regions. Rainfall over northeast (NE) and North India (EI) has been modulated by ENSO-induced SSTA and MSLA over SWIO, thus effecting the total AISMR magnitude. The ENSO-induced changes in heat capacitance (SSTA and MSLA) over SWIO during antecedent months has less impact on west coast of India, central India and North India (NI) rainfall variability. The long-term trend in pre-monsoonal SSTA and MSLA over SWIO shows decreasing rainfall trend over NI, NE, and EI in the recent time. Furthermore, the cooler (warmer) anomaly over the western Indian Ocean affects rainfall variability adversely (favourably) due to the reversal of the wind pattern during the pre-monsoon period. While SSTA and MSLA are increasing in the SWIO, large-scale variability of these parameters during preceding winter and pre-monsoon months combined with surface winds could impact the inter-annual AISMR variability over homogeneous regions of India. Similarly, from an oceanic perspective, the antecedent heat capacitance over SWIO on an inter-annual time scale has been the key to the extreme monsoon rainfall variability.

Remote sensing strategies to monitoring land use maps with AVHRR and MODIS data over the South Asia regions

In South Asia, annual land use and land cover (LULC) is a severe issue in the field of earth science because it affects regional climate, global warming, and human activities. Therefore, it is vitally essential to obtain correct information on the LULC in the South Asia regions. LULC annual map covering the entire period is the primary dataset for climatological research. Although the LULC annual global map was produced from the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset in 2001, this limited the perspective of the climatological analysis. This study used AVHRR GIMMS NDVI3g data from 2001 to 2015 to randomly forests classify and produced a time series of the annual LULC map of South Asia. The MODIS land cover products (MCD12Q1) are used as data from reference for trained classifiers. The results were verified using the annual map of the LULC time series, and the space-time dynamics of the LULC map were shown in the last 15 years, from 2001 to 2015. The overall precision of our 15-year land cover map simplifies 16 classes, which is 1.23% and 86.70% significantly maximum as compared to the precision of the MODIS data map. Findings of the past 15 years show the changing detection that forest land, savanna, farmland, urban and established land, arid land, and cultivated land have increased; by contrast, woody prairie, open shrublands, permanent ice and snow, mixed forests, grasslands, evergreen broadleaf forests, permanent wetlands, and water bodies have been significantly reduced over South Asia regions.

Design Rainfall Change of Rainwater Source Control Facility to Meet Future Scenarios in Beijing

Rainwater source control facilities are essential to sponge city construction in China. Their size is determined based on historical rainfall data. However, with global warming and rapid urban development, rainfall characteristics have also changed, potentially leading to the failure of rainwater source- control facilities to manage surface water in the future. In this study, the design rainfall's change and spatial distribution are analyzed using historical (1961-2014) observation rainfall data and future (2020-2100) projection data of three CMIP6 climate models. The results show that EC-Earth3 and GFDL-ESM4 project that future design rainfall will increase. EC-Earth3 projects a significant increase, while MPI-ESM1-2 projects that the design rainfall will decrease significantly. From the perspective of space, the design rainfall isoline in Beijing has always increased from northwest to southeast. In the historical period, the difference in design rainfall in different regions has reached 19 mm, and this regional heterogeneity shows an increasing trend in the future projection of EC-Earth3 and GFDL-ESM4. The difference in design rainfall in different regions is 26.2 mm and 21.7 mm, respectively. Therefore, it is necessary to consider future rainfall changes in the design of rainwater source control facilities. The relationship curve between the volume capture ratio (VCR) of annual rainfall and design rainfall based on the rainfall data of the project site or region should be analyzed to determine the design rainfall of the rainwater source control facilities.

Using machine learning to anticipate tipping points and extrapolate to post-tipping dynamics of non-stationary dynamical systems

The ability of machine learning (ML) models to "extrapolate" to situations outside of the range spanned by their training data is crucial for predicting the long-term behavior of non-stationary dynamical systems (e.g., prediction of terrestrial climate change), since the future trajectories of such systems may (perhaps after crossing a tipping point) explore regions of state space which were not explored in past time-series measurements used as training data. We investigate the extent to which ML methods can yield useful results by extrapolation of such training data in the task of forecasting non-stationary dynamics, as well as conditions under which such methods fail. In general, we find that ML can be surprisingly effective even in situations that might appear to be extremely challenging, but do (as one would expect) fail when "too much" extrapolation is required. For the latter case, we show that good results can potentially be obtained by combining the ML approach with an available inaccurate conventional model based on scientific knowledge.

South Asian black carbon is threatening the water sustainability of the Asian Water Tower

Long-range transport of black carbon from South Asia to the Tibetan plateau and its deposition on glaciers directly enhances glacier melt. Here we find South Asian black carbon also has an indirect effect on the plateau's glaciers shrinkage by acting to reduce the water supply over the southern Tibetan plateau. Black carbon enhances vertical convection and cloud condensation, which results in water vapor depletion over the Indian subcontinent that is the main moisture flux source for the southern Tibetan plateau. Increasing concentrations of black carbon causes a decrease in summer precipitation over the southern Tibetan plateau, resulting in 11.0% glacier deficit mass balance on average from 2007 to 2016; this loss rises to 22.1% in the Himalayas. The direct (accelerated melt) and indirect (mass supply decrease) effects of black carbon are driving the glacial mass decline of the so-called "Asian Water Tower".

Exploring the direct and indirect impacts of climate variability on armed conflict in South Asia

Although numerous studies have examined the effects of climate variability on armed conflict, the complexity of these linkages requires deeper understanding to assess the causes and effects. Here, we assembled an extensive database of armed conflict, climate, and non-climate data for South Asia. We used structural equation modeling to quantify both the direct and indirect impacts of climate variability on armed conflict. We found that precipitation impacts armed conflict via direct and indirect effects which are contradictory in sign. Temperature affects armed conflict only through a direct path, while indirect effects were insignificant. Yet, an in-depth analysis of indirect effects showed that the net impact is weak due to two strong contradictory effects offsetting each other. Our findings illustrate the complex link between climate variability and armed conflict, highlighting the importance of a detailed analysis of South Asia's underlying mechanisms at the regional scale.

Spatial Prediction of Current and Future Flood Susceptibility: Examining the Implications of Changing Climates on Flood Susceptibility Using Machine Learning Models

The main aim of this study was to predict current and future flood susceptibility under three climate change scenarios of RCP2.6 (i.e., optimistic), RCP4.5 (i.e., business as usual), and RCP8.5 (i.e., pessimistic) employing four machine learning models, including Gradient Boosting Machine (GBM), Random Forest (RF), Multilayer Perceptron Neural Network (MLP-NN), and Naïve Bayes (NB). The study was conducted for two watersheds in Canada, namely Lower Nicola River, BC and Loup, QC. Three statistical metrics were used to validate the models: Receiver Operating Characteristic Curve, Figure of Merit, and F1-score. Findings indicated that the RF model had the highest accuracy in providing the flood susceptibility maps (FSMs). Moreover, the provided FSMs indicated that flooding is more likely to occur in the Lower Nicola River watershed than the Loup watershed. Following the RCP4.5 scenario, the area percentages of the flood susceptibility classes in the Loup watershed in 2050 and 2080 have changed by the following percentages from the year 2020 and 2050, respectively: Very Low = -1.68%, Low = -5.82%, Moderate = +6.19%, High = +0.71%, and Very High = +0.6% and Very Low = -1.61%, Low = +2.98%, Moderate = -3.49%, High = +1.29%, and Very High = +0.83%. Likewise, in the Lower Nicola River watershed, the changes between the years 2020 and 2050 and between the years 2050 and 2080 were: Very Low = -0.38%, Low = -0.81%, Moderate = -0.95%, High = +1.72%, and Very High = +0.42% and Very Low = -1.31%, Low = -1.35%, Moderate = -1.81%, High = +2.37%, and Very High = +2.1%, respectively. The impact of climate changes on future flood-prone places revealed that the regions designated as highly and very highly susceptible to flooding, grow in the forecasts for both watersheds. The main contribution of this study lies in the novel insights it provides concerning the flood susceptibility of watersheds in British Columbia and Quebec over time and under various climate change scenarios.

Response of Indian summer monsoon rainfall to remote carbonaceous aerosols at short time scales: Teleconnections and feedbacks

The effect of atmospheric aerosols on Indian monsoon is one of the scientifically challenging and societally relevant research issues of the recent decades. Past studies have derived inferences mostly based on local emissions and their impacts thereupon. However, more recent studies have shown that the remote effects driven by aerosols elsewhere could also impact the monsoon system on different time scales. Our study using an atmospheric general circulation model (AGCM) shows that regional carbonaceous aerosol emissions (from North America, Europe and North Africa and Asia) can significantly alter Indian summer monsoon rainfall. It is interesting to note that the effects of remote aerosols are larger and bear a resemblance to each other in comparison to local emissions. Our study reveals that the modulation of large-scale circulation induced by regional warming by carbonaceous aerosols leads to teleconnection patterns around the globe, thereby changing the precipitation depending on the phase of these disturbances. We also find that the effects of remote carbonaceous aerosols are strengthened by modulation/feedback through natural dust aerosols over the Arabian Sea with subsequent increase in rainfall over India. The results signify that the changes in the aerosol emissions in one region could lead to the change in precipitation over other regions through global teleconnection and associated feedbacks induced by regional atmospheric warming and/or cooling.

Variation characteristics and the impact of urbanization of extreme precipitation in Shanghai

With the rapid development of urbanization, the characteristics of extreme precipitation in urban areas have changed significantly. Revealing the spatial and temporal distribution of extreme precipitation under changing environment is the basis of scientific response to the urban flood. Trends of extreme precipitation at 95% and 99% thresholds in Shanghai and the influence of urbanization on them were analyzed. The results show that: (1) The precipitation threshold limit value for each site are 5.7 ~ 6.3 mm at 95% with a variation factor of 0.04, and 14.3 ~ 17.16 mm at 99% with a variation factor of 0.06. The precipitation thresholds under 99% conditions were more significantly different among stations. (2) The extreme precipitation at each site has been increasing over the past 50 years, and the growth rates of 95% and 99% extreme precipitation are 8.02~11.46%/10a and 7.11~16.86%/10a, respectively. The growth rate of extreme precipitation is significantly higher than that of average precipitation, while the extreme part of the precipitation probability distribution increases considerably. There is a strong variability in extreme precipitation in this region, while the 99% threshold precipitation varies more. (3) The extreme precipitation in Shanghai is significantly positively correlated with the urbanization of the area around the site. Urbanization has an increasing effect on regional extreme precipitation, with more extreme precipitation and greater growth rate in highly urbanized areas.

Dominant patterns of seasonal precipitation variability in association with hydrological extremes over the North-west Himalayas

The association of hydrological extremes to a specific season provides a perception that the extraction of dominant patterns of seasonal precipitation variability can be useful to identify the hidden pathways of oceanic-atmospheric mechanisms behind these extremes. The native objective of this study is to find the empirical orthogonal function (EOF) patterns of seasonal precipitation using daily gridded precipitation data over the study region. The spatio-temporal variability of monthly precipitation reveals that over the entire study region precipitation occurs throughout the year with less in November and more in August. We found two dominant EOF patterns of precipitation for summer (JJA) and fall (SON) that have captured all the observed floods from 1901 to 2018. The PC of the dominant pattern of summer season precipitation variability (EOF3) shows a significant negative correlation with the El Niño-Southern Oscillation (ENSO) index (Niño 3.4) depicting that global teleconnection influences the variability of JJA precipitation over this region, while the PC of the dominant pattern of SON season precipitation variability (EOF2) has captured the 2014 deadliest flood which is positively correlated with ENSO at < 5% significance level and can be considered a positive domain response of SON precipitation to the variability of SST over the tropical Pacific Ocean (ENSO). The study will find its applicability in predicting the response of hydrological extremes to global teleconnections and hence can be applied in disaster mitigation and decision-making for the water resource management over the study region.

Why empresses have more sons? Maternal instant social condition determines it

Abstract

Sexual selection echoed by the sex ratio is a critical issue in evolution and reproductive biology studies, and the second sex ratio (sex ratio at birth, SRB) is an important evaluation indicator for sex regulation. However, broad debates on sex ratio at birth exist due to the lack of a clear spatiotemporal genealogical database. This study explicitly tests the Trivers and Willard’s hypothesis stating that parents with good social conditions tend to show a male-biased SRB. Using a database of Chinese imperial families from 211BC to 1912 (2142 years) which avoids the spatiotemporal confusion of data thanks to its clear boundaries and long timespan, we found that a proportion of males at birth was 0.54. In particular, the results indicate that the empresses generated a significantly higher male-biased SRB than the concubines within the imperial harems (0.61 vs 0.53), while the SRB of concubines was not higher than ordinary people (0.53 vs 0.52). A significant difference of SRB before and after empress coronation (0.48 vs 0.65) was detected, indicating that the change to a higher social status is the leading cause of a biased SRB. These findings suggest that mothers with privileged instant social conditions tend to generate more boys than girls. In other words, a higher maternal social rank during the conception period, instead of rich resources, forms the primary mechanism regulating the SRB.

Significance statement

Adaptive sex ratio has been a debatable topic difficult to clearly verify since the publication of Trivers and Willard Hypothesis in 1973, which proposes that parents who have good conditions should produce more male offspring. The one reason is that the validity and sample size of the databases used contained unavoidable confounding noise, both genealogically and genetically. To overcome these issues, we specifically compiled a historical database of Chinese imperial families, which are characterized by a confined mating harem and unique eunuch system, guaranteeing biological and genetic purity with precise genealogical relationships and genetic linkages between the parents and the offspring. Thus, this is an extraordinary effort to clarify the hypotheses proposed by TWH and other hypotheses.

Changes in temperature and precipitation extremes in the arid regions of China during 1960–2016

Extreme climate events have a greater impact on natural and human systems than average climate. The spatial and temporal variation of 16 temperature and nine precipitation extremal indices was investigated using the daily maximum and minimum surface air temperature and precipitation records from 113 meteorological stations in China’s arid regions from 1960 to 2016. The warmth indices [warm spell duration (WSDI); numbers of warm nights, warm days, tropical nights (TR), and summer days (SU)] increased significantly. On the contrary, the cold indices [numbers of frost days (FD), ice days (ID), cool days, and cool nights; cold spell duration (CSDI)] decreased significantly. The number of FD decreased fastest (−3.61 days/decade), whereas the growing season length (GSL) increased fastest (3.17 days/decade). The trend was strongest for diurnal temperature range (DTR) (trend rate = −7.29, P &lt; 0.001) and minimum night temperature (trend rate = 7.70, P &lt; 0.001). The cold extreme temperature events increased with increasing latitude, but the warm extreme temperature events decreased. Compared with temperature indices, the precipitation indices exhibited much weaker changes and less spatial continuity. Overall, changes in precipitation extremes present wet trends, although most of the changes are insignificant. The regionally averaged total annual precipitation for wet days increased by 4.78 mm per decade, and extreme precipitation events have become more intense and frequent during the study period. The spatial variability of extreme precipitation in the region was primarily influenced by longitude. Furthermore, the climate experienced a warm-wet abrupt climate change during 1990s.

The rise of Indian summer monsoon precipitation extremes and its correlation with long-term changes of climate and anthropogenic factors

The trends of extreme precipitation events during the Indian summer monsoon measured by two different indicators have been analyzed for the period of 1901–2020, covering the entire India in 9 regions segregated by a clustering analysis based on rainfall characteristics using the Indian Meteorological Department high-resolution gridded data. In seven regions with sufficiently high confidence in the precipitation data, 12 out of the 14 calculated trends are found to be statistically significantly increasing. The important climatological parameters correlated to such increasing trends have also been identified by performing for the first time a multivariate analysis using a nonlinear machine learning regression with 17 input variables. It is found that man-made long-term shifting of land-use and land-cover patterns, and most significantly the urbanization, play a crucial role in the prediction of the long-term trends of extreme precipitation events, particularly of the intensity of extremes. While in certain regions, thermodynamical, circulation, and convective instability parameters are also found to be key predicting factors, mostly of the frequency of the precipitation extremes. The findings of these correlations to the monsoonal precipitation extremes provides a foundation for further causal relation analyses using advanced models.

Amplified risk of compound heat stress-dry spells in Urban India

Compound warm-dry spells over land, which is expected to occur more frequently and expected to cover a much larger spatial extent in a warming climate, result from the simultaneous or successive occurrence of extreme heatwaves, low precipitation, and synoptic conditions, e.g., low surface wind speeds. While changing patterns of weather and climate extremes cannot be ameliorated, effective mitigation requires an understanding of the multivariate nature of interacting drivers that influence the occurrence frequency and predictability of these extremes. However, risk assessments are often focused on univariate statistics, incorporating either extreme temperature or low precipitation; or at the most bivariate statistics considering concurrence of temperature versus precipitation, without accounting for synoptic conditions influencing their joint dependency. Based on station-based daily meteorological records from 23 urban and peri-urban locations of India, covering the 1970-2018 period, this study identifies four distinct regions that show temporal clustering of the timing of heatwaves. Further, combining joint probability distributions of interacting drivers, this analysis explored compound warm-dry potentials that result from the co-occurrence of warmer temperature, scarcer precipitation, and synoptic wind patterns. The results reveal 50-year severe heat stress solely based on the temperature at each location tends to be more frequent and is expected to become 5 to 17-year compound warm-dry events considering interdependence between attributes. Notably, considering dependence among drivers, a median 6-fold amplification (ranging from 3 to 10-fold) in compound warm-dry spell frequency is apparent relative to the expected annual number of a local (univariate) 50-year severe heatwave episode, indicating warming-induced desiccation is already underway over most of the urbanized areas of the country.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00382-022-06324-y.

Machine learning-based assessment of long-term climate variability of Kerala

Studies on historical patterns of climate variables and climate indices have attained significant importance because of the increasing frequency and severity of extreme events worldwide. While the recent events in the tropical state of Kerala (India) have drawn attention to the catastrophic impacts of extreme rainfall events leading to landslides and loss of human lives, a comprehensive and long-term spatiotemporal assessment of climate variables is still lacking. This study investigates the long-term trend analysis (119 years) of climate variables at 5% significance level over the state using gridded datasets of daily rainfall (0.25° × 0.25° spatial resolution) and temperature (1° × 1° spatial resolution) at annual and seasonal scales (south-west monsoon, north-east monsoon, winter and summer). Five trend analysis techniques including the Mann-Kendall test (MK), three modified Mann-Kendall tests and innovative trend analysis (ITA) test were performed in the study. It is evident from the trend analysis results that more than 83% of grid points were showing negative trends in annual and south-west monsoon season rainfall series (at a mean rate of 39.70 mm and 28.30 mm per decade respectively). All the trend analysis tests identified statistically significant increasing trends in mean and maximum temperature at annual and seasonal scales (0.10 to 0.20 °C/decade) for all grids. The K-means clustering algorithm delineated 59 grid points into five clusters for annual rainfall, illustrating a clear geographical pattern over the study area. There is a clear gradient in rainfall distribution and concentration inside the state at annual as well as seasonal scales. The majority of annual rainfall is concentrated in a few months of the year. That may lead the state vulnerable to water scarcity in non-rainy seasons.

One-Pot Synthesis of Rubber Seed Shell-Derived N-Doped Ultramicroporous Carbons for Efficient CO2 Adsorption

In this work, a series of novel rubber seed shell-derived N-doped ultramicroporous carbons (NPCs) were prepared by one-step high-temperature activation (500–1000 °C), using melamine as the nitrogen source and KOH as the activator. The effects of the melamine dosage and the activation temperatures on the surface chemical properties (doped N contents and N species), textural properties (surface area, pore structure, and microporosity), CO₂ adsorption capacities, and CO₂/N₂ selectivity were thoroughly investigated and characterized. These as-prepared NPCs demonstrate controllable BET surface areas (398–2163 m²/g), ultramicroporosity, and doped nitrogen contents (0.82–7.52 wt%). It was found that the ultramicroporosity and the doped nitrogens significantly affected the CO₂ adsorption and the separation performance at low pressure. Among the NPCs, highly microporous NPC-600-4 demonstrates the largest CO₂ adsorption capacity of 5.81 mmol/g (273 K, 1.0 bar) and 3.82 mmol/g (298 K, 1.0 bar), as well as a high CO₂/N₂ selectivity of 36.6, surpassing a lot of reported biomass-based porous carbons. In addition, NPC-600-4 also shows excellent thermal stability and recycle performance, indicating the competitive application potential in practical CO₂ capture. This work also presents a facile one-pot synthesis method to prepare high-performance biomass-based NPCs.

Rising surface pressure over Tibetan Plateau strengthens indian summer monsoon rainfall over northwestern India

The dipole pattern (wetting over northwestern India and drying over the Indo-Gangetic plains and northeast India) in the rainfall trends is reported in many earlier studies. The exact cause of the rainfall trends' asymmetry remains unclear. We show that increasing trends over the northwestern parts are closely associated with the rise in surface pressure over the Tibetan Plateau. The surface pressure over Tibetan Plateau shows increasing trends (0.23 hPa decade^-1, p < 0.01) during 1979-2020. Easterlies across northwest India and southerlies over east India show rises of - 0.26 ms^-1 decade^-1 and 0.15 ms^-1 decade^-1, respectively, in line with Tibetan surface pressure trends. Water vapour transfer across northwest India has increased as a result of these changes in circulation. Increased lower-level easterlies carried more water vapour from the Bay of Bengal over northwest India. At the same time, stronger mid-level southerlies drove extratropical dry air out of India, strengthening the rainfall generating mechanism. Rising easterlies in northwest India also enhance vorticity along the monsoon trough, which promotes rainfall generation. Concurrently, because of the high surface pressure over Tibet, the circulation intensity of the mid-tropospheric cyclone over East India was weakened, resulting in less rain in the Indo-Gangetic region. The present study proposes that an increase in the surface pressure over Tibetan Plateau is an important factor contributing to the dipole pattern in the ISMR trends, particularly upward trends in rainfall over northwest India.

Climatic Trends of Variable Temperate Environment: A Complete Time Series Analysis during 1980–2020

The western Himalayan region is susceptible to minor climate changes because of its fragile ecology, which might threaten the valley’s prestigious ecosystems and socio-economic components. The Himalayas’s local climate and weather are vulnerable to and interlinked with world-scale climatic changes since the region’s hydrology is predominantly dominated by snow and glaciers. The Himalayas, notably the Jammu and Kashmir region in the western Himalayas, has clearly shown distinct and robust evidence of climate change. This study used observed data to examine the climatic variability and trends of change in precipitation and temperature for the Kashmir valley between 1980 and 2020. Gulmarg, Pahalgam, Kokernag, Qazigund, Kupwara, and Srinagar (Shalimar) meteorological stations in the Kashmir valley were studied in detail for long- and short-term as well as localized fluctuations in temperature and precipitation. The annual temperature and precipitation fluctuations were calculated using Sen’s slope approach, and the sloping trend was determined using linear regression. The research showed statistically insignificant growing trends in maximum and minimum temperatures throughout the Kashmir valley. The average annual temperature in the Kashmir valley increased by 1.55 °C during the last 41 years (from 1980 to 2020), with a higher rise in maximum and minimum temperature by 2.00 and 1.10 °C, respectively. However, precipitation showed a non-significant decreasing trend concerning time series analysis over 1980 to 2020 in Kashmir valley. Results of annual average maximum temperature at all the stations revealed that Pahalgam (2.2 °C), Kokernag (1.8 °C), and Kupwara (1.8 °C) displayed a steep upsurge and statistically significant trends; however, annual average minimum temperature followed an increasing trend from 1980 to 2020 at all the stations except Shalimar. However, non-significant declining trends in precipitation were recorded at all the locations in Kashmir valley. This changing pattern of temperature and precipitation could have significant environmental consequences, affecting the western Himalayan region’s food security and ecological sustainability.

“Satark”: Landslide Prediction System over Western Ghats of India

Mountains on the west coast of India are known as the Western Ghat (WG). The WG region has a landslide (LS) susceptibility index of four and is prone to LSs in the monsoon season due to rainfall activity. The LS study focuses on the area between 15.5–20.5° N, 72.5–77.0° E in the Maharashtra state. A catalog of 115 LS events in the study area has been prepared by collecting LS data for 17 years (2000–2016) from various sources. The area from the windward to the leeward side of the WG mountains is divided into three regions: (1) the windward region (72.5–73.4° E) (90 km width), (2) the immediate lee side (ILS) (73.40–74.20° E) (80 km width), and (3) distant lee side (DLS) (74.2–77.0° E) (280 km width). The Center for Citizen Science (CCS), Pune, India, developed the LS-predicting model “Satark” using data from satellites, the India Meteorological Department weather forecasts, radar products, synoptic conditions, and atmospheric sounding data from the Wyoming site for inferring conditions for a hydraulic jump on the WG. The model validation for the 5 years (2017–2021) showed a reasonably good Heidke skill score of 0.44. The model showed 76.5% success in LS prediction 1 day in advance. It is the first attempt of this kind in the Indian region.

Runoff and soil erosion in the integrated farming systems based on micro-watersheds under projected climate change scenarios and adaptation strategies in the eastern Himalayan mountain ecosystem (India)

Land degradation caused by soil erosion (SE) in forests converted into cropland under climate change, particularly with increased rainfall intensity, is of great concern to the agricultural sustainability of the tropical mountain ecosystem. We evaluated the response of six hilly micro-watersheds (HMW) under different Integrated Farming Systems (IFSs) to SE in multi-model climate change scenarios using the Water Erosion Prediction Project (WEPP) model. The IFSs were forestry (HMW₁), abandoned shifting cultivation (HMW₂), livestock with fodder crops (HMW₃), agroforestry (HMW₄), agri-horti-silvi-pastoral (HMW₅), and horticulture (HMW₆) established on a hilly slope (32.0-53.2%) of the eastern Himalayas (Meghalaya, India). The WEPP model was calibrated and validated with measured runoff and soil loss data of 24 years for each of the six IFSs. The projected annual SE (average) for all HMWs increased in all RCPs. The IFS based on shifting cultivation (HMW₂) was the most vulnerable, with the highest percentage increase in SE (46-235%) compared to the baseline years (1976-2005) under RCP 8.5. The cultivated IFSs (HMW₃ to HMW₆) had 47.8-57.0% less runoff and 39.2-74.6% less soil loss than HMW₂ under RCP 8.5. Of these, HMW₆ followed by HMW₄ and HMW₅ were the most effective at minimizing soil loss. Simulation results showed a reduction in soil loss through adaptive strategies such as mulching with broom grasses, stones, field beans, and the introduction of subsurface drainage. Adoption of IFS based on horticulture and agroforestry with bio-mulching on steep slopes is an effective measure to control soil erosion in the eastern Himalaya (India).

Precipitation and Temperature Climatologies over India: A Study with AGCM Large Ensemble Climate Simulations

This study investigated the precipitation and temperature climatologies over India from large ensemble (100 members) historical climate simulations in two recent past climate periods (1951–1980 and 1981–2010). The main focus was to statistically examine the usefulness of such large historical climate simulations by discussing (1) the precipitation and temperature climatologies and their distribution patterns, (2) the annual cycle of the temperature and precipitation climatologies, and (3) the frequency distributions and potential spatial patterns of climate variability. We applied empirical orthogonal function to understand the characteristics and normal probability distribution function to investigate the frequency. Results indicated good agreements of these large ensembles simulated results with the observation over Indian region. The precipitation amount over many regions of India is decreased and temperature over entire India is increased in 1981–2010 compared to that in 1951–1980. The annual cycle of the precipitations over India indicated a decrease of the precipitation amounts from June through October, while an increase of precipitation for the months from November through January. The annual cycle of the temperature over India indicated an increase of temperature during July through March. The frequency distributions of monthly precipitations and temperatures indicate an overall decrease of precipitation and an overall increase of temperature in recent climate period. The reason of decreased precipitation in recent climate period is attributed to a decrease of relative humidity and cloud together with weaker vertical velocity over Indian region during 1981–2010. Overall study validates the usefulness of these large ensemble climate simulations for the assessment of climate over India and suggests that these datasets may be used for various purposes related to weather and climate over India.

Spatio-Temporal Analysis of Rainfall Dynamics of 120 Years (1901–2020) Using Innovative Trend Methodology: A Case Study of Haryana, India

As we know, climate change and climate variability significantly influence the most important component of global hydrological cycle, i.e., rainfall. The study pertaining to change in the spatio-temporal patterns of rainfall dynamics is crucial to take appropriate actions for managing the water resources at regional level and to prepare for extreme events such as floods and droughts. Therefore, our study has investigated the spatio-temporal distribution and performance of seasonal rainfall for all districts of Haryana, India. The gridded rainfall datasets of 120 years (1901 to 2020) from the India Meteorological Department (IMD) were categorically analysed and examined with statistical results using mean rainfall, rainfall deviation, moving-average, rainfall categorization, rainfall trend, correlation analysis, probability distribution function, and climatology of heavy rainfall events. During each season, the eastern districts of Haryana have received more rainfall than those in its western equivalent. Rainfall deviation has been positive during the pre-monsoon season, while it has been negative for all remaining seasons during the third quad-decadal time (QDT3, covering the period of 1981–2020); rainfall has been declining in most of Haryana’s districts during the winter, summer monsoon, and post-monsoon seasons in recent years. The Innovative Trend Analysis (ITA) shows a declining trend in rainfall during the winter, post-monsoon, and summer monsoon seasons while an increasing trend occurs during the pre-monsoon season. Heavy rainfall events (HREs) were identified for each season from the last QDT3 (1981–2020) based on the available data and their analysis was done using European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis Interim (ERA-Interim), which helped in understanding the dynamics of atmospheric parameters during HREs. Our findings are highlighting the qualitative and quantitative aspects of seasonal rainfall dynamics at the districts level in Haryana state. This study is beneficial in understanding the impact of climate change and climate variability on rainfall dynamics in Haryana, which may further guide the policymakers and beneficiaries for optimizing the use of hydrological resources.

A Physical Mechanism for the Indian Summer Monsoon—Arctic Sea-Ice Teleconnection

Significant changes in the Arctic climate, particularly a rapid decline of September Arctic sea ice has occurred over the past few decades. Though the exact reason for such drastic changes is still unknown, studies suggest anthropogenic drivers, natural variability of the climate system, and a combination of both as reasons. The present study focus on the influence of one of the natural variabilities of the climate system, the teleconnections associated with the Indian Summer Monsoon (ISM), and its relationship to September Arctic sea ice. Using 50 years (1951–2000) of National Center for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) NCEP/NCAR reanalysis data, APHRODITE precipitation data, Gridded Monthly Sea Ice Extent and Concentration, 1850 Onward, V2, and HadISST sea-ice concentration data, it is shown that during many strong (weak) ISM years, the Arctic sea ice increased (decreased) predominantly over the Chukchi and Beaufort Seas. The ISM plays a significant role in causing a positive (negative) North Atlantic Oscillation (NAO) during strong (weak) ISM years through the monsoon-desert mechanism associated with monsoonal heating. Simultaneously, the NAO during a strong (weak) ISM causes weakening (strengthening) of the Beaufort Sea High (BSH). The strength of the BSH modulates the Arctic atmospheric circulation, advecting cold air and the direction of the transpolar drift stream, both leading to the generation of more (less) sea ice over the Chukchi-Beaufort Sea region during strong (weak) ISM years. The study illustrates a new atmospheric teleconnection between the tropics and the Arctic.

Flood-Pulse Variability and Climate Change Effects Increase Uncertainty in Fish Yields: Revisiting Narratives of Declining Fish Catches in India’s Ganga River

River-floodplains support a significant number of small-scale capture fisheries despite having undergone degradation due to human modification of river flows by dams, pollution, and climate change. River fish production is underpinned by the annual flood-pulse and associated environmental changes that act as cues for spawning and dispersal for most species. However, studies on fish stock declines have focused more on overfishing than on hydroclimatic variability. Therefore, understanding how changes in flood-pulse variability influence fishing effort and yields is critical to inform adaptive fisheries’ management. We investigated hydroclimatic factors driving flood-pulse variability and fish catch–effort dynamics in India’s Ganga River over two decades (2000–2020). We compiled fishers’ narratives of changing fish catches through semi-structured interviews to compare them with our observed trends. Flood amplitude showed increasing variability, longer duration, and earlier rise timings, linked to La Niña and El Niño phases. Catches per unit effort were correlated with total yield and effort but did not decline as fishers thought, despite overall declines in yield over time. Hydroclimatic variability was a more significant driver of changing yields than local fishing pressure. Rising uncertainty in fisheries’ production, in response to increasing flood-pulse variability and altered flows in the Gangetic Plains, may be affecting fishing behaviour and underlying resource conflicts.

Higher sea surface temperature in the Indian Ocean during the Last Interglacial weakened the South Asian monsoon

Understanding the drivers of South Asian monsoon intensity is pivotal for improving climate forecasting under global warming scenarios. Solar insolation is assumed to be the dominant driver of monsoon variability in warm climate regimes, but this has not been verified by proxy data. We report a South Asian monsoon rainfall record spanning the last ∼130 kyr in the Ganges–Brahmaputra–Meghna river catchment. Our multiproxy data reveal that the South Asian monsoon was weaker during the Last Interglacial (130 to 115 ka)—despite higher insolation—than during the Holocene (11.6 ka to present), thus questioning the widely accepted model assumption. Our work implies that Indian Ocean warming may increase the occurrence of severe monsoon failures in South Asia.

Addressing and anticipating future South Asian monsoon changes under continuing global warming is of critical importance for the food security and socioeconomic well-being of one-quarter of the world’s population. However, climate model projections show discrepancies in future monsoon variability in South Asian monsoon domains, largely due to our still limited understanding of the monsoon response to warm climate change scenarios. Particularly, climate models are largely based on the assumption that higher solar insolation causes higher rainfall during similar warm climatic regimes, but this has not been verified by proxy data for different interglacial periods. Here, we compare Indian summer monsoon (ISM) variability during the Last Interglacial and Holocene using a sedimentary leaf wax δD and δ¹³C record from the northern Bay of Bengal, representing the Ganges–Brahmaputra–Meghna (G-B-M) river catchment. In combination with a seawater salinity record, our results show that ISM intensity broadly follows summer insolation on orbital scales, but ISM intensity during the Last Interglacial was lower than during the Holocene despite higher summer insolation and greenhouse gas concentrations. We argue that sustained warmer sea surface temperature in the equatorial and tropical Indian Ocean during the Last Interglacial increased convective rainfall above the ocean but dampened ISM intensity on land. Our study demonstrates that besides solar insolation, internal climatic feedbacks also play an important role for South Asian monsoon variability during warm climate states. This work can help to improve future climate model projections and highlights the importance of understanding controls of monsoonal rainfall under interglacial boundary conditions.

Differential response of Bay of Bengal during deficit and normal monsoons

Present study explains the disparity in biological production (primary and secondary) with respect to two distinct monsoonal regimes in the western Bay of Bengal, viz., deficit monsoon (DM) and normal monsoon (NM). A combination of in situ and satellite data during the years 2002 (DM) and 2003 (NM) was used to address the physico-chemical and biological responses. The basin was relatively warm (ΔSST = 0.55 °C) and fresh (ΔSSS =  -0.55) during DM than NM. Physical processes such as coastal upwelling and cyclonic eddy were prominent during NM. Variations of hydrography between DM and NM were reflected in the biological production as well. Chlorophyll a concentration (0.05 to 5.2 mg m^-2) was almost similar during both the periods; however, column production showed higher values during DM. Mesozooplankton standing stock was relatively less during DM than NM. Composition of zooplankton also showed pronounced variation between the periods. This can be attributed to the variability in biological response of the region to the varying environmental condition. Relatively high chlorophyll a and primary productivity observed during DM may be due to the increased light availability (less cloud cover) and extended euphotic depth. In addition, the low mesozooplankton standing stock (low grazing) observed during the period also might have helped in maintaining a relatively high rate of production. The seasonal production of the basin is influenced by duration and intensity of various atmospheric as well as oceanic processes.

7 February Chamoli (Uttarakhand, India) Rock-Ice Avalanche Disaster: Model-Simulated Prevailing Meteorological Conditions

The present study aims to analyze the high-resolution model-simulated meteorological conditions during the Chamoli rock-ice avalanche event, which occurred on 7 February 2021 in the Chamoli district of Uttarakhand, India (30.37° N, 79.73° E). The Weather Research and Forecasting (WRF) model is used to simulate the spatiotemporal distribution of meteorological variables pre- and post-event. The numerical simulations are carried out over two fine resolution nested model domains covering the Uttarakhand region over a period of 2 weeks (2 February to 13 February 2021). The model-simulated meteorological variables, e.g., air temperature, surface temperature, turbulent heat flux, radiative fluxes, heat and momentum transfer coefficients, specific humidity and upper wind patterns, were found to show significant departures from their usual patterns starting from 72 h until a few hours before the rock-ice avalanche event. The average 2 m air and surface temperatures near the avalanche site during the 48 h before the event were found to be much lower than the average temperatures post-event. In-situ observations and the ERA5-Land dataset also confirm these findings. The total turbulent heat flux mostly remained downward (negative) in the 72 h before the event and was found to have an exceptionally large negative value a few hours before the rock-ice avalanche event. The model-simulated rainfall and Global Precipitation Measurement (GPM, IMERG)-derived rainfall suggest that the part of the Himalayan region falling in the simulation domain received a significant amount of rainfall on 4 February, around 48 h prior to the event, while the rest of the days pre- and post-event were mostly dry. The results presented here might be helpful in further studies to identify the possible trigger factors of this event.

Freshwater discharge from the large and coastal peninsular rivers of India: A reassessment for sustainable water management

This study offers an updated mean annual water discharge of 10 large and 11 coastal basins of the Indian Peninsula and looks into environmental parameters influencing the water flux and discharge trends. The mean annual discharge of large and coastal rivers is estimated to be 221 and 294 km³. Thus, despite draining 25% of the Indian Peninsula, coastal rivers deliver more than half of the annual flux, and west-flowing coastal rivers contribute 85% of it. This study demonstrates temporal changes in the water discharge of various river basins. The presence of dams regulates discharge regimes of large rivers. The construction of large dams resulted in a significant decline in the water discharge of the Krishna, Cauvery, and Narmada. Through this study, we demonstrate the role of rainfall, catchment size, water loss through evapotranspiration and infiltration, and societal use of water in determining the runoff of each basin. We recommend tapping the water resources of the west-flowing rivers for proper planning, development, and management to reduce the water stress in the peninsular region and promoting sustainable management.

Response of Precipitation in Tianshan to Global Climate Change Based on the Berkeley Earth and ERA5 Reanalysis Products

Global climate change has readjusted a global-scale precipitation distribution in magnitude and timing. In mountainous areas, meteorological stations and observation data are very limited, making it difficult to accurately understand the response of precipitation to global climate change. Based on ECMWF Reanalysis v5 precipitation products, Berkeley Earth global temperature, and typical atmospheric circulation indexes, we integrated a gradient descent-nonlinear regression downscaling model, cross wavelet transform, and wavelet correlation method to analyze the precipitation response in Tianshan to global climate change. This study provides a high-resolution (90 m × 90 m) precipitation dataset in Tianshan and confirms that global warming, the North Pacific Pattern (NP), the Western Hemisphere Warm Pool (WHWP), and the Atlantic Multidecadal Oscillation (AMO) are related to the humidification of Tianshan over the past 40 years. The precipitation in Tianshan and global temperature have a resonance period of 8–15 months, and the correlation coefficient is above 0.9. In Tianshan, spring precipitation is determined mainly by AMO, North Tropical Atlantic Sea Level Temperature, Pacific Interdecadal Oscillation (PDO), Tropical North Atlantic Index, WHWP, NP, summer by NP, North Atlantic Oscillation, and PDO, autumn by AMO, and winter by Arctic Oscillation. This research can serve the precipitation forecast of Tianshan and help in the understanding of the regional response to global climate change.

Deciphering the role of meteorological parameters controlling the sediment load and water discharge in the Sutlej basin, Western Himalaya

The Sutlej River basin of the western Himalaya (study area), owing to its unique geographical disposition, receives precipitation from both the Indian summer monsoon (ISM) and the Westerlies. The characteristic timing and intensity of the ISM and Westerlies, leaves a distinct footprint on the sediment load of the River. Analysis with the last forty years data, shows an increasing trend for temperature. While for precipitation during the same period, the Spiti watershed on the west has highest monthly accumulated precipitation with long term declining trend, in contrast to the other areas where an increasing trend has been observed. Thus, to probe the hydrological variability and the seasonal attributes, governed by the Westerlies and ISM in the study area, we analyzed precipitation, temperature, snow cover area (in %), discharge, suspended sediment concentration (SSC) and suspended sediment load (SSL) for the period 2004 - 2008. To accomplish the task, we used the available data of five hydrological stations located in the study area. Inter-annual shift in peak discharge during the monsoon period is controlled by the variation in precipitation, snow melt, glacier melt and temperature. Besides seasonal variability has been observed in generation of the sediments and its delivery to the river. Our analysis indicates, dominance of the Westerlies footprints in the hydrological parameters of the Spiti region, towards western part of the study area. While, it is observed that the hydrology of the Khab towards eastern part of the study area shows dominance of ISM. Further downstream, the hydrology of Nathpa station also shows dominance of ISM. It also emerged out that the snowmelt contribution to the River flow is mostly during the initial part, at the onset of the monsoon, while for rest and major part of the summer monsoon season, the River flow is augmented by the precipitation, glacial melt and some snow melt. We observed, that the SSC increases exponentially in response to increase in temperature and correlates positively with River discharge. The average daily SSL in the summer monsoon is many times more than that in the winter monsoon. The downstream decrease in steepness of the sediment rating curve is attributed to either a change in the River-sediment dynamics or on account of the anthropogenic forcing. The top 1% of the extreme summer monsoon events (only 4 events) in our study area contribute up to 45% of SSL to the total sediment load budget. It has also been observed that the River-sediment dynamics in the upstream catchments are more vulnerable and sensitive to the extreme events in comparison to the downstream catchments. The present study for the first time gives a holistic insight in to the complex dynamics of the hydrological processes operational in the study area. The research findings would be crucial for managing the water resources of the region and the linked water and food security.

Mapping the spatial and temporal variability of flood hazard affected by climate and land-use changes in the future

The predicts current and future flood risk in the Kalvan watershed of northwestern Markazi Province, Iran. To do this, 512 flood and non-flood locations were identified and mapped. Twenty flood-risk factors were selected to model flood risk using several machine learning techniques: conditional inference random forest (CIRF), the gradient boosting model (GBM), extreme gradient boosting (XGB) and their ensembles. To investigate the future (year 2050) effects of changing climates and changing land use on future flood risk, a general circulation model (GCM) with representative concentration pathways (RCPs) of the 2.6 and 8.5 scenarios by 2050 was tested for impacts on 8 precipitation variables. In addition, future land uses in 2050 was prepared using a CA-Markov model. The performances of the flood risk models were validated with Receiver Operating Characteristic-Area Under Curve (ROC-AUC) and other statistical analyses. The AUC value of the ROC curve indicates that the ensemble model had the highest predictive power (AUC = 0.83) and was followed by GBM (AUC = 0.80), XGB (AUC = 0.79), and CIRF (AUC = 0.78). The results of climate and land use changes on future flood-prone areas showed that the areas classified as having moderate to very high flood risk will increase by 2050. Due to the changes occurring with land uses and in climates, the area classified as moderate to very high risk increased in the predictions from all four models. The areal proportion classes of the risk zones in 2050 under the RCP 2.6 scenario using the ensemble model have changed of the following proportions from the current distribution Very Low = -12.04 %, Low = -8.56 %, Moderate = +1.56 %, High = +11.55 %, and Very High = +7.49 %. The RCP 8.5 scenario has caused the following changes from the present percentages: Very Low = -14.48 %, Low = -6.35 %, Moderate = +4.54 %, High = +10.61 %, and Very High = +5.67 %. The results of current and future flood risk mapping can aid planners and flood hazard managers in their efforts to mitigate impacts.

Resilience of the Central Indian Forest Ecosystem to Rainfall Variability in the Context of a Changing Climate

Understanding the spatio-temporal pattern of natural vegetation helps decoding the responses to climate change and interpretation on forest resilience. Satellite remote sensing based data products, by virtue of their synoptic and repetitive coverage, offer to study the correlation and lag effects of rainfall on forest growth in a relatively longer time scale. We selected central India as the study site. It accommodates tropical natural vegetation of varied forest types such as moist and dry deciduous and evergreen and semi-evergreen forests that largely depend on the southwest monsoon. We used the MODIS derived NDVI and CHIRPS based rainfall datasets from 2001 to 2018 in order to analyze NDVI and rainfall trend by using Sen’s slope and standard anomalies. The study observed a decreasing rainfall trend over 41% of the forests, while the rest of the forest area (59%) demonstrated an increase in rainfall. Furthermore, the study estimated drought conditions during 2002, 2004, 2009, 2014 and 2015 for 98.2%, 92.8%, 89.6%, 90.1% and 95.8% of the forest area, respectively; and surplus rainfall during 2003, 2005, 2007, 2011, 2013 and 2016 for 69.5%, 63.9%, 71.97%, 70.35%, 94.79% and 69.86% of the forest area, respectively. Hence, in the extreme dry year (2002), 93% of the forest area showed a negative anomaly, while in the extreme wet year (2013), 89% of forest cover demonstrated a positive anomaly in central India. The long-term vegetation trend analysis revealed that most of the forested area (>80%) has a greening trend in central India. When we considered annual mean NDVI, the greening and browning trends were observed over at 88.65% and 11.35% of the forested area at 250 m resolution and over 93.01% and 6.99% of the area at 5 km resolution. When we considered the peak-growth period mean NDVI, the greening and browning trends were as follows: 81.97% and 18.03% at 250 m and 88.90% and 11.10% at 5 km, respectively. The relative variability in rainfall and vegetation growth at five yearly epochs revealed that the first epoch (2001–2005) was the driest, while the third epoch (2011–2015) was the wettest, corresponding to the lowest vegetation vigour in the first epoch and the highest in the third epoch during the past two decades. The study reaffirms that rainfall is the key climate variable in the tropics regulating the growth of natural vegetation, and the central Indian forests are dominantly resilient to rainfall variation.

Reservoir bathymetry and riparian corridor assessment in two dammed sections of the Teesta River in Eastern Himalaya

As of mid-2021, four hydroelectric dams are operational on the main channel of the Teesta River in the mountainous and tectonically active Sikkim-Darjeeling-Kalimpong region of India. Riparian ecological and fluvial morphological changes after damming have not been documented. This paper describes an early study of a section of the middle Teesta River, at two of the dam-created reservoirs, just before the river enters the plains. High-resolution, multi-beam, geo-located sonar was used to map the bathymetry of the reservoirs. This resulted in the creation of 30cm-resolution bathymetric maps of the two reservoirs showing valley bottom morphology within them. The bathymetric maps were compared with pre-dam digital elevation models of the valley to create topographic change-maps. The change-maps indicate significant differences in valley morphology due to erosion and deposition processes. Land cover changes due to inundation were quantified from analysis of satellite imagery time series data of the reservoir riparian zones. Land cover change analysis showed a loss of ~ 74,000 trees in ~ 225 ha of flooded riparian corridors due to long-term inundation. The study shows that the dams have caused 7.4% of the river length to become quasi-lentic, and drastically altered sediment dynamics and hydrologic flow. Sediment deposition calculations indicate the reservoirs losing almost three-quarters of their surface areas to sediment deposition features within 15 years. This study will serve as an important baseline for future studies, and influence design and policy regarding riparian and fluvial ecosystem management, monitoring, and evaluation in the Teesta and similar mountainous river basins in the Eastern Himalaya.