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Climate change impact on the habitat distribution of umbrella species presents a critical threat to the entire regional ecosystem. This is further perilous if the species is economically important. Sal (Shorea robusta C.F. Gaertn.), a climax forest forming Central Himalayan tree species, is one of the most valuable timber species and provides several ecological services. Sal forests are under threat due to over-exploitation, habitat destruction, and climate change. Sal's poor natural regeneration and its unimodal density-diameter distribution in the region illustrate the peril to its habitat. We, modelled the current as well as future distribution of suitable sal habitats under different climate scenarios using 179 sal occurrence points and 8 bioclimatic environmental variables (non-collinear). The CMIP5-based RCP4.5 and CMIP6-based SSP245 climate models under 2041–2060 and 2061–2080 periods were used to predict the impact of climate change on sal's future potential distribution area. The niche model results predict the mean annual temperature and precipitation seasonality as the most influential sal habitat governing variables in the region. The current high suitability region for sal was 4.36% of the total geographic area, which shows a drastic decline to 1.31% and 0.07% under SSP245 for 2041–60 and 2061–80, respectively. The RCP-based models predicted more severe impact than SSP; however, both RCP and SSP models showed complete loss of high suitability regions and overall shift of species northwards in the Uttarakhand state. We could identify the current and future suitable habitats for conserving sal population through assisted regeneration and management of other regional issues. [ABSTRACT FROM AUTHOR]

The Himalayan region is likely particularly exposed to climate change indicated by the high regional rate of change. The number of high-resolution, well-calibrated, and long-term paleoclimate reconstructions are however regrettably few, to set this change in a longer-term context. The dendroclimatic reconstructions over Himalaya that do exist have only reconstructed summer season temperatures, and rarely or never attempted to reconstruct mean annual temperatures. The paucity of long meteorological records is a matter of concern when developing chronologies of climate sensitive tree-ring data in Bhutan, but the chronologies would theoretically be of high potential for extending short meteorological records back in time using trees in high-elevation ecotones. The objectives of this study were to explore dendroclimatic signals in tree-ring width chronologies of Abies densa growing in these extreme ecotones and to reconstruct, if possible, annual temperatures over Northern Bhutan. A point-by-point regression analysis revealed that the regional composite chronology was significantly and positively correlated with temperatures of all months of the current year, i.e., January to December. The chronology was highly correlated with annual temperatures (calibration period R = 0.67 and validation period R = 0.50; p < 0.001) allowing a reconstruction of temperature over Northern Bhutan (NB-TEMR). The NB-TEMR reveals some common variations with summer temperature reconstructions of the Northern Hemisphere as well as the Himalayan region, particularly w.r.t to the recent warming trend. The reconstruction covers the period of 1765 to 2017. This reconstruction reveals a warming trend since 1850 with higher rates of warming 1935 to 2017, but with a pause around 1940–1970. The warming is consistent with reduced volcanic activity and increase of greenhouse gases. We anticipate that our new reconstruction of annual mean temperature could be an important contribution for future climate change studies and assessments of climate models. [ABSTRACT FROM AUTHOR]

This study investigated the impacts of climate and socio-economic changes on water resources in the Beas-Sutlej river basin, Himalaya. Multiple-model ensembles of five climate models forced with the Representative Concentration Pathway (RCP) 8.5 scenario and the Shared Socio-economic Pathway SSP 1 were used. A validated Water Evaluation And Planning (WEAP) model of the basin was used for assessing the resulting sectoral water allocations. The results showed increasing runoff during the pre-monsoon and monsoon seasons, principally as the respective consequences of increased glaciers melting and higher rainfall. The finding also indicates that ensemble mean annual irrigation water demand will decrease by between 8 and 13% in Punjab due to the conversion of agricultural land to urban centres but will increase in Rajasthan by about 14%. These outcomes will be useful for the Bhakra and Beas Management Board in developing adaptation strategies to cope with any future water shortages that may occur in the basin. [ABSTRACT FROM AUTHOR]

The Indian summer monsoon is an integral part of the global climate system. As its seasonal rainfall plays a crucial role in India's agriculture and shapes many other aspects of life, it affects the livelihood of a fifth of the world's population. It is therefore highly relevant to assess its change under potential future climate change. Global climate models within the Coupled Model Intercomparison Project Phase 5 (CMIP5) indicated a consistent increase in monsoon rainfall and its variability under global warming. Since the range of the results of CMIP5 was still large and the confidence in the models was limited due to partly poor representation of observed rainfall, the updates within the latest generation of climate models in CMIP6 are of interest. Here, we analyze 32 models of the latest CMIP6 exercise with regard to their annual mean monsoon rainfall and its variability. All of these models show a substantial increase in June-to-September (JJAS) mean rainfall under unabated climate change (SSP5-8.5) and most do also for the other three Shared Socioeconomic Pathways analyzed (SSP1-2.6, SSP2-4.5, SSP3-7.0). Moreover, the simulation ensemble indicates a linear dependence of rainfall on global mean temperature with a high agreement between the models independent of the SSP if global warming is the dominant forcing of the monsoon dynamics as it is in the 21st century; the multi-model mean for JJAS projects an increase of 0.33 mm d -1 and 5.3 % per kelvin of global warming. This is significantly higher than in the CMIP5 projections. Most models project that the increase will contribute to the precipitation especially in the Himalaya region and to the northeast of the Bay of Bengal, as well as the west coast of India. Interannual variability is found to be increasing in the higher-warming scenarios by almost all models. The CMIP6 simulations largely confirm the findings from CMIP5 models, but show an increased robustness across models with reduced uncertainties and updated magnitudes towards a stronger increase in monsoon rainfall. [ABSTRACT FROM AUTHOR]

The selection of global climate models (GCMs) for a region remained a difficult step in climate change studies. A state-of-the-art Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithm is proposed in this paper for GCM selection. The ranking of GCMs obtained using SVM-RFE was compared to that obtained using entropy-based similarity assessment index known as Symmetrical Uncertainty (SU). The study was conducted in the sub-Himalayan region of Pakistan where a reliable projection of climate is highly significant for water resources management in the entire western part of South Asia. The RF-based regression model was employed to generate a multi-model ensemble (MME) mean of the top-ranked GCMs. The MME mean projection was utilized to estimate the spatiotemporal changes in annual precipitation in comparison with precipitation of 1961‐–2000 for various representative concentration pathway (RCP) scenarios. The SVM-RF selected five GCMs (MIROC5, EC-EARTH, CNRM-CM5, BCC-CSM1.1(m) and BCC-CSM1.1) as most suitable for climate change projections in the study area. Obtained results were found to collaborate well with the results of multiple conventional statistical metrics. The MME mean projections revealed precipitation alteration between −1% and 18% during 2020‐–2059, and 0 and 24% during 2060–2099 for different RCPs. Precipitation was projected to increase up to 20% in the north whereas a decrease up-to −16% in the south. • SVM-RFE algorithm is used for selection of GCMs for Upper Indus Basin (IUB). • Performance of SVM-RF was compared with symmetrical uncertainty (SU) and conventional statistics based methods. • SVM-RFE was found to select the same GCMs as selected by SU and conventional statistics. • Precipitation projections in IUB revealed high heterogeneity in future changes in precipitation. [ABSTRACT FROM AUTHOR]