Uttarakhand, a hill state of India, covers an area of 51,125 km$^{2}$. The geographic position is highly crucial with in the Central Himalayas (CH), for agro-climate, water resource management, food-processing, tourism, etc., having enriched bio-diversity and forest. Present study investigates the spatio-temporal characteristics and distribution of temperature of Uttarakhand state. Observation and model (under different Representative Concentration Pathways (RCPs) at radiative forcing 2.6, 4.5 and 8.5 W/m$^{2}$) temperature fields are studied to assess the present and future trends. Standard temperature fields from AphroTemp, Climate Research Unit (CRU) and ECMWF Reanalysis-Interim (ERA-Interim) are used. Attempt is to find orographic responses on the surface temperature at seasonal scale. Elevation dependent warming (EDW) is higher at higher elevations as compared to lower elevations. In particular, it reaches to maximum during Indian summer monsoon months (JJAS) as estimated from AphroTemp during 1970–2007. Munsiyari region experiences highest warming rate by 0.038$^{\circ}$C/decade. Elevational temperature trends show higher increase with statistical significance at 99% confidence level from <500 to 3000 m elevation belt during JJAS. For elevation >3000 m, highest warming trend is observed during MAM. Further, temperature trends analysed using one of the regional climate models REMO of the CORDEX-SA suite, depict an increase by 0.019$^{\circ}$C/yr. Future temperature trends under RCP2.6, RCP4.5 and RCP8.5 show warming trends by 0.008$^{\circ}$, 0.022$^{\circ}$, and 0.064$^{\circ}$C/yr, respectively.

$\bf{Highlights}$

$\bullet$ Provide process information on temperature across and along the foothills of Himalayas during climate change

$\bullet$ Elevation dependent changes in the temperature

$\bullet$ Orographic interactions.

In the recent past, there were extensive floods in the Western Himalayan region (WHR) due to continuous long spells of heavy rainfall for 3–4 days that caused a huge loss in life and property over the region. WHR is a data sparse region, with limited meteorological stations having a continuous long spell of daily precipitation data. In the present study, spatial and temporal variability of seasonal as well as annual precipitation, precipitation days and maximum accumulated daily, 2 days, 3 days, 4 days and 5 days precipitation over WHR is considered by using daily precipitation data of 18 meteorological stations of the region. Out of 18 meteorological stations, five stations have continuous data from 1901 to 1980 and remaining 13 stations data is considered for their common period from 1981 to 2014. Accordingly, the analysis is carried out in two parts, first for 1901–1980 (for 5 stations) and second for 1981–2014 (for all the 18 stations). The analysis suggests high variability in the spatial and temporal distribution of seasonal as well as maximum accumulated daily to 5 days precipitation over WHR. In general, increasing trends in maximum accumulated precipitation in lower altitude stations and decreasing trends in higher altitude stations are observed in monsoon season and vice-versa in the winter season during the period 1981–2014. The increase in maximum accumulated daily to 5 days precipitation is up to 9.7 mm per decade during 1901–1980 and is up to 45.5 mm per decade during 1981–2014 in monsoon season in lower altitude stations. Thus, the increase in maximum accumulated precipitation during monsoon season becomes manifold during 1981–2014 as compared to the period 1901–1980.

With the changing climate, reliable climate projections are essential for agriculture risk management. The present study aims to explore the output of a regional climate model (RCM) at different climatic regimes and its applications in crop simulation models. Here, a comparative study of the cotton crop growth and yield response for Akola in the central and Hisar northern agroclimatic zone of India represents rainfed and irrigated growing regions of cotton, respectively. The RegCM4 projections and its bias-corrected values of temperature and precipitation data for the period 1971–2005 are compared with the observations to assess its reliability with the crop simulation models as weather inputs. The results signify that the RCM model is wet, which implies that, it shows high rainfall intensity in terms of frequency as a number of rainy days and amount. The model also shows night warming as there is a significant decline in maximum temperature and minimal decline in minimum temperature, thus there is a reduced diurnal temperature difference. Overall model highly underestimates temperature and overestimates rainfall. Strikingly reduced numbers of intense warm and cold events are simulated. Model is highly biased for rainfall events${/ge}$0 mm/day and 5mm/day, and moderately biased for rainfall${/ge}$5 mm/day. Precipitation bias-correction, using quantile mapping approach, shows excellent agreement at an annual scale. But precipitation variability could not be captured that well as it is a ‘distribution-based method’. However, it worked well in the irrigated Hisar region than the rainfed Akola region. The bias-corrected RegCM4 climate inputs are utilized in Decision Support System for Agro-technology Transfer (DSSAT) simulations for cotton yields, Leaf Area Index (LAI) and ball number at maturity/m$^2$ (NM) for both regions. Bias-corrected outputs are in better agreement with corresponding observations than non-bias-corrected outputs in both regions. Future research could apply these simulated model data complemented with reliable bias correction techniques to explicitly study climate change’s impact on crop productivity.

Predicting the impacts of future climate on food and fibre production are essential for devising suitable adaptations strategy. This study aims to understand the impact of climate change on cotton crop change using Regional Climate Model (RCM) in the near and far future. The RCM model considered for the study is RegCM4 from CORDEX-SA experiment for the two RCP scenarios at RCP4.5 and RCP8.5. To refine these projections, we have bias-corrected the data using quantile mapping approach. This study is based on two locations: one in Hisar (mostly irrigated) and the other in Akola (mostly rainfed), located in the northern and central agro-climatic zones for cotton. The daily projected data have been summarised as 1971–2005(1990), 2006–2035(2020), 2036–2065(2050) and 2066–2095(2080). The RCM projections show good agreement with the observed climatology, but still bias exists, which is fine-tuned by bias-correction. The RCM model showed reduced diurnal temperature and night warming as it highly underestimates maximum temperature and slightly the minimum temperature. It also predicts rise in temperature at higher rates in northern than central zones. Also, the amount of rainfall is increasing in the northern region and decreasing in the central region at RCP8.5. The spatial variability is observed as the amount of rainfall is increasing in the northern irrigated region and decreasing in the central rainfed region. The rainfall intensity in Hisar is projected to increase till 2050 and a further decline in 2080. And in the central zone, it is presently higher than the northern region, but projected to decrease further from 1990 to 2080. These daily weather data were then employed in the cotton-CROPGRO model under DSSAT-CSM (v4.6) to assess its impact on future climate. The crop model has been simulated with these weather projections for the three sowing dates under rainfed, irrigated, and potential conditions. It is observed that the simulated crop yields and LAI in Akola are higher at RCP8.5 than RCP4.5, whereas in Hisar, it is lower at RCP8.5 than RCP4.5. So, in the cooler and wetter central zone, temperature may slightly rise at RCP8.5 along with increased rainfall and CO$_2$, favouring the cotton crop. This shows the suitability of crops in this region even at RCP8.5 and far future. Whereas in the hot and dry northern agro-climatic cotton zone, it is projected that the temperature slightly increases from present at RCP4.5 and further at RCP8.5, and the amount of rainfall increases at RCP4.5 and reduces at RCP8.5. So, the crop here could stand the increased temperature at RCP4.5 and is also favoured due to increased CO$_2$ and precipitation. But, at RCP8.5, the comparatively higher rate of increase in temperature and decreasing amount of rainfall may affect cotton crops adversely, with its maximum effects in the far future. Also, in future climate with temporal variability in the amount of precipitation and increasing temperature, late sown cotton crops are favoured, especially with proper irrigation practices in both the regions. The study embraces utilisation of RCMs and crop models to study the vulnerability of crops to climate change, which could help to assess the site-specific adaptive potential and mitigation measures for future climate.

Permafrost in the Upper Indus Basin (UIB) in Ladakh, India, is a critical water source and is less studied. Identifying permafrost and its characteristics is a crucial knowledge gap in the UIB. Thus, understanding the permafrost active layer dynamics is critical and essential due to its implications on regional hydrology, infrastructure stability, and disaster occurrence. For this purpose, an experimental site is prepared with 11 plots having two near-surface ground temperature loggers each, i.e., 22 in total, in the upper Ganglass catchment, a sub-region of the UIB, Ladakh. The permafrost active layer thickness characteristics and its thaw progression are simulated using the 1-D GEOtop model with forcing from these 22 loggers from 2016 to 2020. The snow days are calculated using the near-surface ground temperature. The simulation results show no permafrost at 4727 m a.s.l. consistently, whereas all the plotsabove 4900 m a.s.l. show permafrost active layer thickness, in particular, up to 4 m at 4942 m a.s.l.Permafrost characteristics significantly differ between a warmer (colder) year with low (high) snow. The mean surface offset of the catchment ranges between 0.01°and 5.5C°. These findings on permafrost and associated periglacial processes will provide a critical knowledge base for the stability of high-elevation infrastructure, glacial lakes, regional hydrology and climate, particularly for water.