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Conceptual models demonstrate that climate can control the structural evolution of mountain belts and that Pliocene–Quaternary global climate change modulated Himalayan wedge behaviour. A key test of this hypothesis is the timing of foreland accretion. If foreland accretion pre‐dates climate change, then climate is not the primary driver of mountain building. New apatite (U–Th)/He data from NW Himalaya foreland thrust sheets suggest that accretion had occurred by the latest 4 Ma, prior to the Pliocene–Quaternary global climate change. Climate cannot, therefore, be invoked as the principal driver of Himalayan tectonics in the late Cenozoic. Statement of significance: This paper presents 66 new, single grain apatite (U–Th)/He (AHe) dates from the foreland basin that constrain NW Himalayan evolution through the late Cenozoic. We show that accretion of the NW sub‐Himalayan foreland was underway by 4 Ma at the latest, several million years earlier than previous estimates, and was followed by distributed, wedge‐internal deformation by 2 Ma. These and other published data present a consistent pattern and timing of deformation observed over nearly 600 km of the NW sub‐Himalaya. Our new constraints on orogen evolution provide key insights into the relative importance of climate and tectonics in driving orogenic systems. [ABSTRACT FROM AUTHOR]

Western Disturbances (WDs) are the primary contributor of winter precipitation over the Western Himalayas (WH). The precipitation falling as snow is essential for Himalayan glaciers accumulation, contributing to Himalayan rivers supporting downstream population of the Indian subcontinent. Climate change has affected the Himalayas, which is noticeable through lesser snowfall and higher melt leading to critical water resource stresses and managements. Available climate models are one of the best available tools to assess precipitation changes over and around the Himalayas. Inherent uncertainties and errors in these numerical models need a certain urge for improvement. Integration of statistical models and methods on top of dynamical models provide better insight and utility in associated processes. Thus, a statistical relationship between various predictors and precipitation over the region is investigated. Available observations from APHRODITE and ERA‐I and model fields from EC‐EARTH‐RCA4 (a CORDEX‐SA member) is used. Multiple linear regression (MLR) model is used to establish statistical estimates using meteorological variables as predictors and precipitation. The correlation between the predictors and precipitation is determined, and a contour depicting maximum correlation is used to estimate regression coefficients. The MLR model is trained during present (1987–2005) and tested during 2006–2007. Higher positive correlations between observations and MLR model are found. This developed MLR model is then implemented for assessing future WDs under RCP8.5 scenario. The intensity and frequency of WDs are calculated during the present and future. Model‐based WDs' intensities slightly deviate from the corresponding observations in present. But the model‐based WDs' frequencies match with the corresponding observations in present. The results indicate that WDs show a decreasing trend in frequency and intensity in the present and near‐future. But in far‐future intensity of WDs shows an increasing trend. [ABSTRACT FROM AUTHOR]

The Hindu Kush Himalaya region in Nepal is considered hydrologically vulnerable and fragile, and is likely to face adverse impacts due to climate change. Many rivers originate in this region, which are the lifeline for the millions of riparian people. The Marsyangdi river basin is the hub of hydroelectric power generation. The main objective of this study is to assess the impact of climate change in water availability by using the Soil and Water Assessment Tool (SWAT) model. Future climate projections were derived from three regional climate models (RCMs), namely ACCESS1‐CCAM (ACCESS) CNRM‐CM5‐CCAM (CNRM), and MPI‐ESM‐LR‐CSIRO‐CCAM under two representative concentration pathways, RCP 4.5 and RCP 8.5. These projections were bias‐corrected against the observed historical data, and simulation of impact was assessed using the SWAT model. This study reveals an increase in maximum temperature by 1.4°C by the 2040s (2031–50) and 2.6°C by the 2070s (2061–80) and in minimum temperature by 1.8 and 2.9°C by the 2040s and 2070s, respectively. The future projected rainfall shows a declining trend in all the models. The maximum increase in annual rainfall in the CNRM model is about 31.1% for RCP 8.5 during the 2040s and the MPI RCP 8.5 model show a minimum reduction of about 19.6% in the 2070s. In all three models the average annual rainfall is expected to increase by 10.2 and 10.7% by the 2040s and 2070s respectively. The reduction in streamflow in 2031–2050 is maximally about 19% in the CNRM RCP 8.5 and minimally about 6.3% in the MPI RCP 8.5. For the period 2061–2080, the CNRM RCP 8.5 shows a maximum decrease in streamflow of about 18.9% and the MPI RCP 8.5 shows a minimum reduction in streamflow of about 2.6%. This study will give significant insight to the planners for sustainable and integrated management of water resources and for the operation and design of hydropower projects in the basin. [ABSTRACT FROM AUTHOR]

ABSTRACT The spatial climatic characteristics of the Himalayas are complex and a challenge for regional climate models ( RCMs). There is no doubt that some form of correction before any application of RCM simulations is a must. In recent years, simple bias correction techniques have been overshadowed by more popular and complex bias correction techniques. In this study an attempt is made to compare the performance of a simple and of a comparatively complex correction technique for hydrological analysis at a monthly resolution in the Kaligandaki River Basin of Nepal. The research workflow consists of bias correction of temperature and precipitation using a simple technique (linear scaling) and a comparatively complex one (quantile mapping). The performance at monthly resolution is evaluated against observed meteorological data while a combined evaluation is made via hydrological model response analysis. The wetter and colder RCM estimates were significantly improved after bias correction. The hydrological modelling response also shows the importance of the bias correction of the RCMs. However, no significant difference was observed between the outputs of linear scaling and quantile mapping which exhibited almost identical performances. Hence, this study has a novel conclusion that a simple method, such as linear scaling, is sufficient for hydrological analysis at monthly resolution. [ABSTRACT FROM AUTHOR]

ABSTRACT The western Himalayas (WH) is characterized by heterogeneous land surface characteristics and topography. During winter (December, January, and February-DJF), eastward moving low-pressure synoptic weather systems, called Western Disturbances (WDs) in Indian parlance, cause the majority of the precipitation mostly in the form of snow. The interplay between land surface/topography and WDs greatly controls precipitation distribution over the region. This study seeks to evaluate this using a mosaic-type parameterization of subgrid-scale topography and land use (sub-BATS) for regional climate simulation with a regional climate model (RegCM3). The model coarse grid cell size in the control simulation is 60 km while the subgrid cell size is 10 km. This study compares two 22 year simulations (1980-2001) during winter (DJF). The first simulation is without (CONT) and the second is with (SUB) the fine scale subgrid scheme. Representing the fine scale processes using the subgrid scheme SUB experiment simulates reduced precipitation by approximately 2 mm d−1 with comparison to CONT experiment. This estimation of reduced and closer to the corresponding observed precipitation is important for regional water budget over the WH which is primarily governed by topographic and land surface disaggregation. Validation with corresponding observations over similar elevations shows that SUB displays an improvement over CONT experiment. This relevant decrease of precipitation in SUB using disaggregation-reaggregation methodology for initial model input fields in subBATS scheme is due to better representation of the WH topography. In case of temperature, SUB experiment displays colder bias (∼2-4 °C) than the CONT over the Himalayas. This preliminary finding is important for studying regional water balance, snow melt accumulation in following summer period. [ABSTRACT FROM AUTHOR]