Purpose: The study has been carried out to investigate and assess the significance of the potential trend of three variables viz. rainfall, temperature and runoff over the Rangoon watershed in Dadeldhura district of Nepal.Methodology: In this study, trend analysis has been carried out on monthly, seasonal and annual basis using the data period between 1979 to 2010 for rainfall and temperature and 1967 to 1996 for runoff. Mann-Kendall test and Sen’s slope estimate test were applied to identify the existing trend direction and Sen’s slope estimator test were used to detect the trend direction and magnitude of change over time.Main findings: The most important findings are, i) There is warming trends over the Rangoon watershed as Mann-Kendall statistic (Z-value) for most of the maximum temperature values are positive, ii) Rainfall and runoff affected by fluctuations every year though the annual rainfall showing a rising trend whereas runoff showing a falling trend. The rainfall seasonal trend analysis indicates that monsoon and post-monsoon period showed a positive rainfall trend with z statistics of +1.93, and +1.12 respectively, whereas pre-monsoon and winter seasons showed a negative trend with z statistics of -1.02, and -0.54 respectively. However, the annual rainfall in the Rangoon watershed showed a positive trend with a z value of +1.70.Importance of this study: This case study has been undertaken to investigate the trends of important climatic variables viz. rainfall, temperature which have a direct impact on the agriculture of the region.Originality / Novelty of study: This is an original research work undertaken under the M. Tech programme during 2016-17 at IIT Roorkee by the scholar Er. Amar Bahadur Pal from Nepal.

The Narmada river basin is a highly regulated catchment in central India, supporting a population of over 16 million people. In such extensively modified hydrological systems, the influence of anthropogenic alterations is often underrepresented or excluded entirely by large-scale hydrological models. The Global Water Availability Assessment (GWAVA) model is applied to the Upper Narmada, with all major dams, water abstractions and irrigation command areas included, which allows for the development of a holistic methodology for the assessment of water resources in the basin. The model is driven with 17 Global Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble to assess the impact of climate change on water resources in the basin for the period 2031–2060. The study finds that the hydrological regime within the basin is likely to intensify over the next half-century as a result of future climate change, causing long-term increases in monsoon season flow across the Upper Narmada. Climate is expected to have little impact on dry season flows, in comparison to water demand intensification over the same period, which may lead to increased water stress in parts of the basin.

A catastrophic debris flow in the Rishiganga and Dhauliganga rivers in Uttarakhand, India, on 7 February 2021 left a trail of disaster. Around 100–150 people lost their lives according to Uttarakhand Chief Secretary statement given to ANI news portal, two hydropower projects were badly damaged and a bridge across the Rishiganga River was washed off in the event. Study shows that the debris flow is caused due to detachment of 0.59 km² right lobe of a hanging glacier and resultant ice-rock avalanche. This right lobe of the glacier was located over a mountain slope having an average slope of 35° at 4700–5555 m a.s.l. and travelled 12.4 km before hitting the infrastructure projects. Role of precipitation, snow cover, land surface temperature, and permafrost processes were investigated for identifying causes of the event. Since 2012, monsoon precipitation and mean annual land surface temperature (LST) showed significant increasing trend. Snow cover during monsoon months showed increasing trend and September, October and November experienced decreasing trend at glacier elevations. Mean annual LST increased from − 0.3 °C in 2012 to a peak of 0.4 °C in 2016. Central lobe of the glacier advanced during this period and eventually fell off in 2016 suggesting that the LST warming forced reduction of frictional drag at the interface facilitating it advancement and eventual dislodgement. Permafrost modelling suggests warm permafrost below 50 m and conditions favourable for intense frost cracking up to 10–15 m. At ~ 40 m depth, the delayed response of 2012–2016 warming produced peak positive temperature conditions by December and probably facilitated the formation of thin film of water at the deeper layers acting as a lubricant for glacier sliding. It is also suggested that the increase in summer precipitation might have forced thickening of the accumulation area and thereby increasing the shear stress for sliding of the glacier. It is proposed that the recent change in the weather conditions in the region is primarily responsible for this event through geological, glaciological, and permafrost processes. Flood modelling study suggests a flood volume of ~ 10 MCM generating 24.5 m flow depth at the bridge site with 12.7 m/s flow velocity. The event highlighted the need for improved monitoring of the cryosphere areas of the Himalaya to capture the early warning signs for better preparedness.

The commonly used precipitation-based drought indices typically rely on probability distribution functions that can be suitable when the data exhibit minimal discrepancies. However, in arid and semi-arid regions, the precipitation data often display significant discrepancies due to highly irregular rainfall patterns. Consequently, imposing any probability distributions on the data for drought analysis in such regions may not be effective. To address this issue, this study employs a novel drought index called the Discrepancy Precipitation Index (DPI), specifically designed for arid regions. Unlike traditional methods, the DPI does not impose a probability distribution on the precipitation data; instead, it relies on the discrepancy between the data and the mean value. Drought severity classifications (i.e., Drought-I, Drought-II, and Drought-III) are proposed based on the DPI values. The DPI is used to characterize and assess the meteorological drought years based on annual and monsoonal precipitation over nineteen districts in Western Rajasthan, India, during 1901–2019. Additionally, a novel statistic called Discrepancy Measure (DM) is employed to assess the degree of discrepancy in the precipitation climatology of the districts for annual and monsoon precipitation time series. Based on annual precipitation, Jaisalmer district exhibited the highest number of historical drought years (35), whereas three districts, i.e., Jhunjhunu, Dausa, and Bhilwara exhibited the lowest number of drought years (11). Similarly, based on monsoon precipitation, Jaisalmer and Bhilwara encountered the highest (34) and the lowest (11) number of drought years, respectively. The return period of Drought-II is lower for monsoon precipitation-based DPI as compared to that of the annual precipitation-based DPI for all the districts. The DM and DPI-based total number of droughts are found to be strongly correlated for both annual and monsoon precipitation. The DM value is highest for Jaisalmer and lowest for Bhilwara district. The findings reveal DPI as an efficient tool for assessing drought years, particularly in arid climatic conditions. Moreover, as the DM value increases for a precipitation series, the DPI becomes more effective in capturing drought events.