Water level dynamics in catchment-scale rivers is an important factor for surface water studies. Manual measurement is highly accurate but inefficient. Using automatic water level sensors has disadvantages such as high cost and difficult maintenance. In this study, a water level recognition method based on digital image processing technology and CNN is proposed. For achieving batch segmentation of source images, the coordinates of the water ruler region in the source image and characters’ region and the scale lines’ region on the ruler are obtained by using image processing algorithms such as grayscale processing, edge detection, and the tilt correction method based on Hough-transform and morphological operations. The CNN is then used to identify the value of digital characters. Finally, the water level value is calculated according to the mathematical relationship between the number of scale lines detected by pixel traversal in the binarized image and the value of digital characters. This method is used to identify the water levels of the water ruler images collected in the Hulu watershed of the Qilian Mountains in Northwest China. The results show that the accuracy compared with the actual measured water level reached 94.6% and improved nearly 24% compared to the template matching algorithm. With high accuracy, low cost, and easy deployment and maintenance, this method can be applied to water level monitoring in mountainous rivers, providing an effective tool for watershed hydrology research and water resources management.

The fluxes of sensible heat (H) and latent heat (LE), which are generally the important parts of the energy and mass balances over glacier surfaces, are widely quantified by the bulk method. However, due to the difficulty of determining the aerodynamic roughness length z_0m in this method, H and LE values may still have large uncertainties with significant inaccuracy. To acquire reliable varying and intrinsic z_0m values, new simpler parameterizations for z_0m values at different ranges of the friction velocity u_*b were fitted in this study. The method was implemented using the related meteorological data and glacial sublimation/condensation measured at the top of the August‐one glacier (4817 m asl) in the Qilian Mountains from 1 July 2016 to 15 August 2017. The parameterization shows that z_0m increased sharply when u_*b exceeded 0.43 m/s (the approximate threshold value) and that the effect of snowdrift was slight for the hourly z_0m values in the range 0.15 ≤ u_*b ≤ 0.43 m/s, which could thus be used to calculate the daily z_0m. During the wet period (1 July to 24 September 2016 and 5 May to 15 August 2017), the turbulent fluxes calculated by the bulk method showed that net radiation R_net was the primary source of surface energy (60.7 w/m²), whereas during the dry period (25 September 2016 to 4 May 2017), the main heat sink was the positive H (28.5 w/m²) rather than the negative R_net (−10.0 w/m²).

Remote sensing techniques provide data on the spatial–temporal distribution of environmental parameters over regions with sparse ground observations. However, the resolution of satellite precipitation data is too coarse to be applied to hydrological and meteorological research at basin scales. Downscaling research using coarse remote sensing data to obtain high-resolution precipitation data is significant for the development of basin-scale research. Here, we propose improvements to a spatial–temporal method for downscaling satellite precipitation. The improved method uses a nonlinear regression model and introduces longitude and latitude based on processed normalized difference vegetation index (NDVI) and a digital elevation model (DEM) to stimulate precipitation in the Qilian Mountains during 2006–2015. The final downscaled annual precipitation (FDAP) results are corrected by observed data to obtain corrected final downscaled annual precipitation (CFDAP) datasets. For temporal downscaling, monthly downscaled data are the corrected monthly ratio multiplied by the corresponding downscaled annual datasets. The results indicated that processed NDVI (PNDVI) reflected spatial precipitation patterns more accurately than the original NDVI. The accuracy was significantly improved when the final downscaled annual precipitation data were corrected by observed data. The average annual root mean square error (RMSE) from 2006 to 2015 of CFDAP was 66.48 and 83.07 mm less than that of FDAP and original Tropical Rainfall Measuring Mission (TRMM) data, respectively. Compared with previous methods, which use NDVI and/or DEM to downscale TRMM, the accuracy of FDAP and CFDAP from the improved method was higher, and the RMSE decreased on average by 13.63 and 80.11 mm. The RMSE of monthly data from corrected monthly ratio (CMR) decreased on average by 4.93 mm over monthly data from previous monthly ratio (PMR). In addition, the accuracy of the original satellite data affected the initial downscaling results but had no significant effects on the corrected downscaling results.

As a principal pathway of moisture loss of cryosphere, the sublimation of snow or ice needs to absorb a high amount of energy to complete the phase transition, while related researches on how sublimation affects surface energy balance (SEB) are still insufficient, causing unclear conclusions. To characterize the seasonal variation of a mountain glacier's sublimation or evaporation and analysis its role on SEB, the meteorological and glaciological data over five‐year measurement period (2016–2020) were obtained on the top of August‐one glacier. In the non‐melt period of a year (January–May and October–December), sensible heat flux H was the dominant energy contributor to glacier surface (76%), followed by net radiation R _net (22%). Sublimation removed average 85 mm (0.4 mm/day) of water vapor from surface for the dry and windy weather, and the latent heat flux of sublimation LEE accounted for 33% of energy sinks (67% of energy was stored in the glacier Q _s ). In the melt period (June–September), evaposublimation (mainly composed of evaporation) reached to 36 mm (0.3 mm/day), but LEE only played a small heat sink (14%) and the Q _m (the energy for melting) had the largest proportion (70%). R _net and H contributed 81% and 15% of surface energy, respectively. Obviously, under the global climate warming, the sublimation of non‐melt period seems to be effective in decrease the heating rate of glacier surface.