As a unique geomorphological unit on Earth, the Qinghai-Tibet Plateau (QTP) is sensitive to global warming, and has warmed twice as fast as the global average over the past five decades (D. Chen et al., 2015;Yao et al., 2019). The QTP, known as the "Third Pole," has the largest area of alpine permafrost in the world (Jin et al., 2000). Permafrost on the QTP has been observed to have degraded substantially as a result of drastic climate warm-
The Qinghai-Tibet Plateau (QTP), the highest plateau in the world with an average elevation of over 4,000 m above sea level (a.s.l.), is characterized by unique topography and geographical location. It is thus known as the "Third Pole." Its thermal and dynamic effects exert profound influence on the regional climate and even the global climate system (Duan & Wu, 2005;Yanai et al., 1992). Many important Asian rivers originate from the QTP, including the Yangtze River, the Yellow River and the Mekong River. It holds the largest ice storage after the polar regions, dubbed as the "Asian Water Tower." It drains a lot of water to rivers and is particularly crucial for water resources on the Asian continent (Immerzeel et al., 2010;Xu et al., 2008;Yao et al., 2019). The QTP covers an area of approximately 2.6 × 10 6 km 2 , half of which is underlain by permafrost and accounts for 75% of alpine permafrost in the Northern Hemisphere (Jin et al., 2000).
In the context of global climate change, the Qinghai-Tibetan plateau (QTP) has experienced unprecedented changes in its local climate. While general circulation models (GCM) are able to forecast global-scale future climate change trends, further work needs to be done to develop techniques to apply GCM-predicted trends at site scale to facilitate local ecohydrological response studies. Given the QTP’s unique altitude-controlled climate pattern, the applicability of the quantile–quantile (Q-Q) adjustment approach for this purpose remains largely unknown and warrants investigation. In this study, this approach was evaluated at 36 sites to ensure the results are representative of different climatic and surface conditions on the QTP. Considering the practical needs of QTP studies, the study aims to assess its capability for downscaling monthly GCM simulations of major variables onto the site scale, including precipitation, air temperature, wind speed, relative humidity, and air pressure, based on two GCMs. The calibrated projections at the sites were verified against the observations and compared with those from two commonly used adjustment methods—the quantile-mapping method and the delta method. The results show that the general trends of most variables considered are well adjusted at all sites, with a quantile pair of 25–75% for all the variables except precipitation where 10–90% is used. The calibrated results are generally close to the observed values, with the best performance in air pressure, followed by air temperature and relative humidity. The performance is relatively limited in adjusting wind speed and precipitation. The accuracies decline as the adjustment extends into the future; a wider adjustment window may help increase the performance for the variables subject to climate changes. It is found that the performance of the adjustment is generally independent of the locations and seasons, but is strongly determined by the quality of GCM simulations. The Q-Q adjustment works better for the meteorological variables with fewer fluctuations and daily extremes. Variables with more similarities in probability density functions between the observations and GCM simulations tend to perform better in adjustment. Generally, this approach outperforms the two peer methods with broader applicability and higher accuracies for most major variables.
Microwave remote sensing techniques provide a direct measurement of surface soil moisture (SM), with advantages for all-weather observations and solid physics. However, most satellite microwave soil moisture products fail to meet the requirements of land surface studies for high-resolution surface soil moisture data due to their coarse spatial resolutions. Although many approaches have been proposed to downscale the spatial resolution of satellite soil moisture products, most of them have been tested in flat areas where the surface is relatively homogeneous. Thus, those established approaches are often inapplicable for downscaling in cold alpine areas with complex terrain where multiple factors control the variations in surface soil moisture. In this work, we re-inferred and verified the mathematical assumption behind a semi-physical approach for downscaling satellite soil moisture data and extended this approach for cold alpine areas. Instead of directly deriving SM from proxy variables, this approach relies on a relationship between two standardized variables of SM and apparent thermal inertia (ATI), in which the sub grid standard deviation for SM is estimated by a physical hydraulic model taking soil texture data as input. The approach was applied to downscale the soil moisture active passive (SMAP) daily data in a typical cold alpine basin, i.e., the Babao River basin located in the Qilian Mountains of Northwest China. We observed good linearity between the computed ATI and SM observations on most wireless sensor network sites installed in the study basin, which justifies the underlying assumption. The sub grid standard deviations for the SMAP grid estimated through the Mualem-van Genuchten model can broadly represent the real characteristics. The downscaled 1-km resolution results correlated well with the in-situ SM observations, with an average correlation coefficient of 0.74 and a small root mean square error (0.096 cm3/cm3). The downscaled results show more and consistent textural details than the original SMAP data. After removal of biases in the original SMAP data even higher agreements with the observations can be achieved. These results demonstrate the adequacy of the proposed semi-physical approach for downscaling satellite soil moisture data in cold alpine areas, and the resultant fine-resolution data can serve as useful databases for land surface and hydrological studies in those areas.
Abstract. Our understanding and predictive capability of streamflow processes largely rely on high-quality datasets that depict a river’s upstream basin characteristics. Recent proliferation of large sample hydrology (LSH) datasets has promoted model parameter estimation and data-driven analyses of the hydrological processes worldwide, yet existing LSH is still insufficient in terms of sample coverage, uncertainty estimates, and dynamic descriptions of anthropogenic activities. To bridge the gap, we contribute the Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) to complement existing LSH datasets, which covers 21,568 watersheds from 13 agencies for as long as 43 years based on discharge observations scraped from web. In addition to annual streamflow indices, each basin’s daily meteorological variables (i.e., precipitation, 2 m air temperature, longwave/shortwave radiation, wind speed, actual and potential evapotranspiration), daily-weekly water storage terms (i.e., snow water equivalence, soil moisture, groundwater percentage), and yearly dynamic descriptors of the land surface characteristics (i.e., urban/cropland/forest fractions, leaf area index, reservoir storage and degree of regulation) are also provided by combining openly available remote sensing and reanalysis datasets. The uncertainties of all meteorological variables are estimated with independent data sources. Our analyses revealed the following insights: (i) the meteorological data uncertainties vary across variables and geographical regions, and the prominent patterns revealed should be accounted for by LSH users, (ii) ~6 % watersheds shifted between human managed and natural states during the GSHA time span, which may be useful for hydrologic analysis that takes the changing land surface characteristics into account, and (iii) GSHA watersheds observed a more widespread declining trend in runoff coefficient than an increasing trend, which warrants further studies on water availability. Overall, GSHA is expected to serve hydrological model parameter estimation and data-driven analyses as it continues to improve. GSHA v1.0 can be accessed at https://doi.org/10.5281/zenodo.8090704 (Yin et al., 2023).
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