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In the high altitude Hindukush Karakoram Himalaya (HKH) mountains, the complex weather system, inaccessible terrain and sparse measurements make the elevation-distributed precipitation and temperature among the most significant unknowns. The elevation-distributed snow and glacier dynamics in the HKH region are also little known, leading to serious concerns about the current and future water availability and management. The Hunza Basin in the HKH region is a scarcely monitored, and snow- and glacier-dominated part of the Upper Indus Basin (UIB). The current study investigates the elevation-distributed hydrological regime in the Hunza Basin. The Distance Distribution Dynamics (DDD) model, with a degree day and an energy balance approach for simulating glacial melt, is forced with precipitation derived from two global datasets (ERA5-Land and JRA-55). The mean annual precipitation for 1997–2010 is estimated as 947 and 1,322 mm by ERA5-Land and JRA-55, respectively. The elevation-distributed precipitation estimates showed that the basin receives more precipitation at lower elevations. The daily river flow is well simulated, with KGE ranging between 0.84 and 0.88 and NSE between 0.80 and 0.82. The flow regime in the basin is dominated by glacier melt (45%–48%), followed by snowmelt (30%–34%) and rainfall (21%–23%). The simulated snow cover area (SCA) is in good agreement with the MODIS satellite-derived SCA. The elevation-distributed glacier melt simulation suggested that the glacial melt is highest at the lower elevations, with a maximum in the elevation 3,218–3,755 masl (14%–21% of total melt). The findings improve the understanding of the local hydrology by providing helpful information about the elevation-distributed meltwater contributions, water balance and hydro-climatic regimes. The simulation showed that the DDD model reproduces the hydrological processes satisfactorily for such a data-scarce basin.

An increase in extreme temperature events could have a significant impact on terrestrial ecosystems. Reanalysis temperature data are an important data set for extreme temperature estimation in mountainous areas with few meteorological stations. The ability of ERA5-Land reanalysis data to capture the extreme temperature index published by the Expert Team on Climate Change Detection and Indices (ETCCDI) was evaluated by using the observational data from 17 meteorological stations in the Qilian Mountains (QLM) during 1979–2017. The results show that the ERA5-Land reanalysis temperature data can capture well for the daily maximum temperature, two warm extremes (TXx and TX90p) and one cold extreme (FD0) in the QLM. ERA5-Land’s ability to capture temperature extremes is best in summer and worst in spring and winter. In addition, ERA5-Land can capture trends in all extreme temperature indices except the daily temperature range (DTR). The main bias of ERA5-Land is due to the difference in elevation between the ground observation station and the ERA5-Land grid point. The simulation accuracy of ERA5-Land increases with the decrease of elevation difference. The results can provide a reference for the study of local extreme temperature by using reanalysis data.

Reanalysis temperature products are important datasets for temperature estimates over high-elevation areas with few meteorological stations. In this study, surface 2 m air temperature data from 17 meteorological stations from 1979 to 2017 in the Qilian Mountains (QLM) are used for comparison with the newest reanalysis product: ERA5-Land derived from the European Centre for Medium-Range Weather Forecasts (ECMWF). In general, the ERA5-Land temperature product can reproduce the observation variation at different time scales very well. A high monthly correlation coefficient that ranges from 0.978 to 0.998 suggests that ERA5-Land reanalysis temperature could capture the observations very well. However, attention should be paid before using ERA5-Land at individual sites because of the average root-mean-square-error (RMSE) of 2.2°C of all stations. The biases between ERA5-Land temperature and observations are mainly caused by the elevation differences between ERA5-Land grid points and meteorological sites. The annual mean temperature shows a significant warming trend (0.488°C/decade) from 1979 to 2017 based on the observations. ERA5-Land reanalysis temperature captures the increasing trend very well (0.379°C/decade). The biggest positive warming trends of observations and ERA5-Land are both found in summer with values of 0.574°C/decade and 0.496°C/decade, respectively. We suggest that ERA5-Land generally reproduces the temperature trend very well for observations and is reliable for scientific research over the QLM.