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In this paper, the digital management of scientific and technological innovation services in universities based on the background of artificial intelligence is studied in depth, aiming to create a comprehensive and efficient management platform. By constructing a platform with a multi-level architecture, it realizes the informatization management of the whole process of university science and technology projects. Specifically, this study designed a functional structure of 10 subsystems, covering all stages from project declaration to results management. To improve the management efficiency, principal component analysis and cluster analysis techniques were introduced, and the effectiveness and feasibility of the platform were verified by analyzing 270 questionnaires. The average score is 3.8293, showing that users highly recognize the platform. In addition, by qualitatively and quantitatively analyzing the factors affecting management, the key factors promoting and inhibiting the management efficiency were clarified, providing a new perspective for the management of scientific and technological innovation services in universities.

The Maxian Mountains, characterized by high altitudes and abundant vegetation, create a cooler and more humid environment compared to the surrounding areas, and are highly susceptible to climate change. In order to study the cold and wet island effects in the Maxian Mountains, air temperature and relative humidity (RH) were analyzed using meteorological station data. Additionally, spatial variations were examined by retrieving Land Surface Temperature (LST) and the Temperature Vegetation Dryness Index (TVDI) from 2001 to 2021. The most pronounced cold island effect was observed in the mountainous area during summer, mainly in May and July. The most significant wet island effect was observed from March to May, with an average relative humidity difference of 24.72%. The cold island area index, as an indicator of the cold island effect, revealed an increasing trend in the summer cold island effect in recent years. The cooling intensity ranged from 5 to 10 °C, with variations observed between 500 and 1000 m. A 30% increase in wet island effects in summer was observed, with a humidification intensity within a range of 500 m. Geodetector analysis identified vegetation cover as the primary factor affecting the thermal environment in mountainous areas. The increase in vegetation in mountainous areas was identified as the main reason for enhancing the cold and wet island effects. The findings emphasize the role of vegetation in enhancing cold and wet island effects, which is crucial for understanding and preserving mountainous regions. [ABSTRACT FROM AUTHOR]

We develop a moving-mesh permafrost model that contains a ground subsidence computation module to estimate the effects of ground subsidence on permafrost simulation under different warming scenarios. Including the ground subsidence process in the permafrost simulation produces only a relatively small improvement in the simulation performance of the ground temperature field, as validated by observations from two sites on the Qinghai–Tibetan Plateau (QTP). It is shown that ignoring ground subsidence tends to achieve a larger active layer thickness (ALT) but a smaller original thickness of permafrost that has thawed when simulating permafrost changes in a warming climate. The heat consumed by permafrost changes will be underestimated in simulations that do not consider ground subsidence. The effects that ground subsidence exerts within the permafrost simulation are clearly demonstrated under a strong warming scenario, which will influence the global energy budget. Projections indicate that the permafrost in the continuous permafrost area of the QTP may be close to the phase transition temperature to become zero thermal gradients in 2030–2040 under the SSP5-8.5 scenario, and there will be a great risk of ground subsidence by that stage. For permafrost regions with rich ground ice, the downward propagating temperature signals caused by ground subsidence are more attenuated. However, the heat calculation error will be larger in a simulation that does not consider ground subsidence there. This study quantifies the effects of ground subsidence, which can provide a better understanding of the permafrost thaw and energy budget of the QTP. [ABSTRACT FROM AUTHOR]

Relict permafrost presents an ideal opportunity to understand the impacts of climatic warming on the ground thermal regime since it is characterized by mean annual ground temperature close to 0 °C and relatively thin permafrost. The long-term and continuous observations of permafrost thermal state and climate background are of great importance to reveal the links between the energy balance on hourly to annual timescales, to evaluate the variations of permafrost thermal state over multi-annual periods and to validate the remote sensing dataset. Until now there are few data available in relict permafrost regions although those data are important to understand the impacts of climate changes on permafrost especially in the boundary regions between permafrost and seasonally frozen ground regions. In this study, we present 11 years of meteorological and soil data in a relict permafrost site of the Mahan Mountain on the northeast of the Qinghai-Tibet Plateau. The meteorological data are comprised of air and ground surface temperature, relative humidity, wind speed and direction, shortwave and longwave downward and upward radiation, water vapor pressure, and precipitation on half-an-hour timescale. The active layer data include daily soil temperature and soil moisture at five different depths. The permafrost data consist of ground temperature at twenty different depths up to 28.4 m. The high-quality and long-term datasets are expected to serve as accurate forcing data in land surface models and evaluate remote-sensing products for a broader geoscientific community. The datasets are available from the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Cryos.tpdc.271838, Wu and Xie, 2021). [ABSTRACT FROM AUTHOR]

Permafrost has great influences on the climatic, hydrological, and ecological systems on the Qinghai–Tibet Plateau (QTP). The changing permafrost and its impact have been attracting great attention worldwide like never before. More observational and modeling approaches are needed to promote an understanding of permafrost thermal state and climatic conditions on the QTP. However, limited data on the permafrost thermal state and climate background have been sporadically reported in different pieces of literature due to the difficulties of accessing and working in this region where the weather is severe, environmental conditions are harsh, and the topographic and morphological features are complex. From the 1990s, we began to establish a permafrost monitoring network on the QTP. Meteorological variables were measured by automatic meteorological systems. The soil temperature and moisture data were collected from an integrated observation system in the active layer. Deep ground temperature (GT) was observed from boreholes. In this study, a comprehensive dataset consisting of long-term meteorological, GT, soil moisture, and soil temperature data was compiled after quality control from an integrated, distributed, and multiscale observation network in the permafrost regions of QTP. The dataset is helpful for scientists with multiple study fields (i.e., climate, cryospheric, ecology and hydrology, meteorology science), which will significantly promote the verification, development, and improvement of hydrological models, land surface process models, and climate models on the QTP. The datasets are available from the National Tibetan Plateau/Third Pole Environment Data Center (https://data.tpdc.ac.cn/en/disallow/789e838e-16ac-4539-bb7e-906217305a1d/ , last access: 24 August 2021, 10.11888/Geocry.tpdc.271107, Lin et al., 2021). [ABSTRACT FROM AUTHOR]

The ground surface soil heat flux (G0) is very important to simulate the changes of frozen ground and the active layer thickness; in addition, the freeze-thaw cycle will also affect G0 on the Tibetan Plateau (TP). As G0 could not be measured directly and soil heat flux is difficult to be observed on the TP in situ due to its high altitude and cold environment, most of previous studies have directly applied existing remote sensing-based models to estimate G0 without assessing whether the selected model is the best one of those models for those study regions. We use in-situ observation data collected at 12 sites combined with Moderate Resolution Imaging Spectroradiometer (MODIS) data (MOD13Q1, MODLT1D, MOD09CMG, and MCD15A2H) and the China meteorological forcing dataset (CMFD-SRad and CMFD-LRad) to validate the main models during the freeze-thaw process. The results show that during the three stages (complete freezing (CF), daily freeze-thaw cycle (DFT), and complete thawing (CT)) of the freeze-thaw cycle, the root mean square error (RMSE) between the models' G0 simulated value and the corresponding G0 "measured value" is the largest in the CT phase and smallest in the CF phase. The simulated results of the second group schemes (SEBAL, Ma, SEBALadj, and Maadj) were slightly underestimated, more stable, and closer to the measured values than the first group schemes (Choudhury, Clawson, SEBS, Choudhuryadj, Clawsonadj, and SEBSadj). The Maadj scheme is the one with the smallest RMSE among all the schemes and could be directly applied across the entire TP. Then, four possible reasons leading to the errors of the main schemes were analyzed. The soil moisture affecting the ratio G0/Rn and the phase shift between G0 and net radiation Rn are not considered in the schemes directly; the scheme cannot completely and correctly capture the direction of G0; and the input data of the schemes to estimate the regional G0 maybe bring some errors into the simulated results. The results are expected to provide a basis for selecting remote sensing-based models to simulate G0 in frozen ground dynamics and to calculate evapotranspiration on the TP during the freeze-thaw process. The scheme Maadj suitable for the TP was also offered in the study. We proposed several improvement directions of remote sensing-based models in order to enhance understanding of the energy exchange between the ground surface and the atmosphere. [ABSTRACT FROM AUTHOR]

Landslides are one of the major geohazards in the Qinghai-Tibet Plateau, and have recently increased in both frequency and size. SAR interferometry (InSAR) has been widely applied in landslide research, but studies on monitoring small-scale landslides are rare. In this study, we investigated the performance of Small Baseline Subsets method (SBAS) in monitoring small-scale landslide and further developed a new deformation model to obtain the absolute deformation time series. The results showed that SBAS could well capture the small-scale landslide characteristics including spatiotemporal abnormal displacement and progressive failure processes. The newly developed absolute deformation model further detected the process of landslide details, such as instances of noticeable creeps induced by rainfall and snowmelt. Finally, a conceptual model of the kinematics-based failure mechanism for small-scale landslide was proposed. This study extended the monitoring capability of InSAR and improved our knowledge on the deformation in the frozen ground regions. [ABSTRACT FROM AUTHOR]