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Karst trough valleys are prone to flooding, primarily because of the unique hydrogeological features of karst landform, which are conducive to the spread of rapid runoff. Hydrological models that represent the complicated hydrological processes in karst regions are effective for predicting karst flooding, but their application has been hampered by their complex model structures and associated parameter set, especially so for distributed hydrological models, which require large amounts of hydrogeological data. Distributed hydrological models for predicting the Karst flooding is highly dependent on distributed structrues modeling, complicated boundary parameters setting, and tremendous hydrogeological data processing that is both time and computational power consuming. Proposed here is a distributed physically-based karst hydrological model, known as the QMG (Qingmuguan) model. The structural design of this model is relatively simple, and it is generally divided into surface and underground double-layered structures. The parameters that represent the structural functions of each layer have clear physical meanings, and the parameters are less than those of the current distributed models. This allows modeling in karst areas with only a small amount of necessary hydrogeological data. 18 flood processes across the karst underground river in the Qingmuguan karst trough valley are simulated by the QMG model, and the simulated values agree well with observations, for which the average value of Nash-Sutcliffe coefficient was 0.92. A sensitivity analysis shows that the infiltration coefficient, permeability coefficient, and rock porosity are the parameters that require the most attention in model calibration and optimization. The improved predictability of karst flooding by the proposed QMG model promotes a better mechanistic depicting of runoff generation and confluence in karst trough valleys. [ABSTRACT FROM AUTHOR]

Air pollution is substantially modulated by meteorological conditions, and especially their diurnal variations may play a key role in air quality evolution. However, the behaviors of temperature diurnal cycles along with the associated atmospheric condition and their effects on air quality in China remain poorly understood. Here, for the first time we examine the diurnal cycles of day-to-day temperature change and reveal their impacts on winter air quality forecasting in mountain-basin areas. Three different diurnal cycles of the preceding day-to-day temperature change are identified and exhibit notably distinct effects on the day-to-day changes in atmospheric dispersion conditions and air quality. The diurnal cycle with increasing temperature obviously enhances the atmospheric stability in the lower troposphere and suppresses the development of the planetary boundary layer, thus deteriorating the air quality on the following day. By contrast, the diurnal cycle with decreasing temperature in the morning is accompanied by a worse dispersion condition with more stable atmosphere stratification and weaker surface wind speed, thereby substantially worsening the air quality. Conversely, the diurnal cycle with decreasing temperature in the afternoon seems to improve air quality on the following day by enhancing the atmospheric dispersion conditions on the following day. The findings reported here are critical to improve the understanding of air pollution in mountain-basin areas and exhibit promising potential for air quality forecasting. [ABSTRACT FROM AUTHOR]

The alpine region is important in riverine and watershed ecosystems as a contributor of freshwater, providing and stimulating specific habitats for biodiversity. In parallel, recent climate change, human activities and other perturbations may disturb hydrological processes and eco-functions, creating the need for next-generation observational and modeling approaches to advance a predictive understanding of such processes in the alpine region. However, several formidable challenges, including the cold and harsh climate, high altitude and complex topography, inhibit complete and consistent data collection where/when needed, which hinders the development of remote sensing technologies and alpine hydrological models. The current study presents a suite of datasets consisting of long-term hydrometeorological, snow cover and frozen ground data for investigating watershed science and functions from an integrated, distributed and multiscale observation network in the upper reaches of the Heihe River Basin (HRB) in China. Gap-free meteorological and hydrological data were monitored from an observation network connecting a group of automatic meteorological stations (AMSs). In addition, to capture snow accumulation and ablation processes, snow cover properties were collected from a snow observation superstation using state-of-the-art techniques and instruments. High-resolution soil physics datasets were also obtained to capture the freeze-thaw processes from a frozen ground observation superstation. The updated datasets were released to scientists with multidisciplinary backgrounds (i.e., cryosphere science, hydrology, and meteorology), and they are expected to serve as a testing platform to provide accurate forcing data and validate and evaluate remote sensing products and hydrological models for a broader community. The datasets are available from the Cold and Arid Regions Science Data Center at Lanzhou https://doi.org/10.3972/hiwater.001.2019.db. [ABSTRACT FROM AUTHOR]