Lake evaporation can influence basin-wide hydrological cycles and is an important factor in loss of water resources in endorheic lakes of the Tibetan Plateau. Because of the scarcity of data, published lake evaporation values are inconsistent, and their spatial distribution has never been reported. Presenting a plausible hypothesis of energy balance during the ice-free seasons, we explored the multiyear (2003–2016) average ice phenology and evaporation amounts of 75 large dimictic lakes by using a combination of meteorological and satellite data. Evaporation amounts show large variability in spatial distribution, with a pattern of higher values in the south. Lakes with higher elevation, smaller area, and higher latitude are generally associated with a shorter ice-free season and lower evaporation. The total evaporated water amounts have values of approximately 29.4 ± 1.2 km³ year⁻¹ for the 75 studied lakes and 51.7 ± 2.1 km³ year⁻¹ for all plateau lakes included.

The southern Tibetan Plateau (STP) is the region in which water vapor passes from South Asia into the Tibetan Plateau (TP). The accuracy of precipitable water vapor (PWV) modeling for this region depends strongly on the quality of the available estimates of water vapor advection and the parameterization of land evaporation models. While climate simulation is frequently improved by assimilating relevant satellite and reanalysis products, this requires an understanding of the accuracy of these products. In this study, PWV data from MODIS infrared and near-infrared measurements, AIRS Level-2 and Level-3, MERRA, ERA-Interim, JRA-55, and NCEP final reanalysis (NCEP-Final) are evaluated against ground-based GPS measurements at nine stations over the STP, which covers the summer monsoon season from 2007 to 2013. The MODIS infrared product is shown to underestimate water vapor levels by more than 20% (1.84 mm), while the MODIS near-infrared product overestimates them by over 40% (3.52 mm). The AIRS PWV product appears to be most useful for constructing high-resolution and high-quality PWV datasets over the TP; particularly the AIRS Level-2 product has a relatively low bias (0.48 mm) and RMSE (1.83 mm) and correlates strongly with the GPS measurements (R = 0.90). The four reanalysis datasets exhibit similar performance in terms of their correlation coefficients (R = 0.87–0.90), bias (0.72–1.49 mm), and RMSE (2.19–2.35 mm). The key finding is that all the reanalyses have positive biases along the PWV seasonal cycle, which is linked to the well-known wet bias over the TP of current climate models.

Ground observation of precipitation over complex terrain is subject to large uncertainties due to inadequate sampling. This study explores a method that combines limited gauge data and a high‐resolution numerical simulation to quantify the precipitation uncertainties in central Himalaya. Specifically, the Coefficient of spatial Variability (CV) of precipitation and the minimum Number of Required Stations (NRS) to obtain areal‐mean precipitation ground truth values within a 0.25° area are investigated using fine‐scale meteorological simulation at 1.5 km grid spacing. Evaluation over a densely‐gauged area demonstrates comparable CV and NRS values between station observations and simulations. The simulated CV and NRS values in a larger area show a strong and positive dependence on each other and an expected positive (negative) correlation with topographic complexity (temporal scale). The proposed method sheds lights on evaluating precipitation products and holds promise for informing the layout of rain gauge networks in complex terrain.

While the Himalayas act as a natural barrier to water vapor transport to the Tibetan Plateau, many north‐south‐oriented valleys channel moisture onto the Plateau. However, owing to the lack of in situ data, the spatiotemporal characteristics of precipitation remain unclear along these valleys. In this study, a high‐altitude (2,800–4,500 m above mean sea level) rain‐gauge network was established in the Yadong Valley, one of the main valleys in the central Himalayan Region (CHR). New observations from this network are used to evaluate the Integrated Multi‐satellitE Retrieval for Global Precipitation Measurement (IMERG) precipitation product, with results demonstrating that the IMERG data can reproduce the seasonal and diurnal patterns of precipitation seen in the observational data. This combination of in situ and IMERG precipitation data reveals two unique characteristics in the CHR. First, precipitation during the premonsoon season (March‐May) contributes 20–40% of the annual total, from the high altitudes of the CHR to Southeast Tibetan Plateau, due to water vapor flux conveyed by southwesterlies from the Bay of Bengal, whereas this percentage is much lower in the northern Himalaya and South Asia. Second, diurnal variations in precipitation during the monsoon season (June‐September) vary between the low and high altitudes of the north‐south‐oriented valleys in the CHR: Precipitation at high altitudes has two peaks (one in the afternoon and the other at night), which is different from the single nighttime peak in low altitudes that is usually reported. These two notable spatiotemporal characteristics are indicative of the unique climate that exists at the high altitudes of the CHR.