This data is a 5km monthly hydrological data set, including grid runoff and evaporation (if evaporation is less than 0, it means condensation; if runoff is less than 0, it means precipitation is less than evaporation), simulated and output through the WEB-DHM distributed hydrological model of the Indus River basin, with temperature, precipitation, barometric pressure, etc. as input data.
The Qinghai Tibet Engineering Corridor starts from Golmud in the north and ends at Lhasa in the south. It passes through the core area of the Qinghai Tibet Plateau and is an important channel connecting the mainland and Tibet. Permafrost temperature is not only an important index to study ground thermal state in permafrost regions, but also a key factor to be considered in permafrost engineering construction. The core of GIPL1.0 is the Kudryavtesv method, which considers the thermophysical properties of snow cover, vegetation and different soil layers. However, Yin found that compared with the Kudryavtesv method, the accuracy of TTOP model was higher. Therefore, the model was improved in combination with the freezing/thawing index. Through the verification of field monitoring data, it was found that the simulation error of permafrost temperature was less than 1 ℃. Therefore, the improved GIPL1.0 model is used to simulate the permafrost temperature of the Qinghai Tibet project corridor, and predict the future permafrost temperature under the SSP2-4.5 climate change scenario.
The active layer thickness in the Wudaoliang permafrost region of the Qinghai Tibet Plateau is retrieved based on the seasonal deformation obtained by SBAS-InSAR technology and ERA5-Land spatio-temporal multi-layer soil moisture data corrected by variational mode decomposition method. The time range of the is 2017-2020, and the spatial resolution is 1km. This data can be used to study the change of the active layer thickness in the permafrost region of the Qinghai Tibet Plateau and analyze its interaction with climate change, water cycle and energy cycle. It is significance to understand the permafrost degradation, environment evolution and the impact of permafrost degradation on ecology and climate.
The Qinghai-Tibet Engineering Corridor runs from Golmud to Lhasa. It passes through the core region of the Qinghai-Tibet Plateau and is an important passage connecting the interior and Tibet. The active layer thickness (ALT) is not only an important index to study the thermal state of ground in permafrost region, but also a key factor to be considered in the construction of permafrost engineering. The core of GIPL1.0 is kudryavtesv method, which takes into account the thermophysical properties of snow cover, vegetation and different soil layers. However, Yin Guoan et al. found that compared with kudryavtesv method, the accuracy of TTOP model is higher, so they improved the model in combination with freezing / thawing index. Through verification of field monitoring data, it was found that the simulation error of ALT is less than 50cm. Therefore, the ALT in the Qinghai Tibet project corridor is simulated by using the improved GIPL1.0 model, and the future ALT under the ssp2-4.5 climate change scenario is predicted.
The data set of ecological adjustment value of Arctic permafrost change from 1982 to 2015, with the time resolution of 1982, 2015 and the change rate of two phases, covers the entire Arctic tundra area, with the spatial resolution of 8km. Based on multi-source remote sensing, simulation, statistics and measured data, and combined with GIS and ecological methods, it quantifies the adjustment service value of Arctic permafrost to the ecosystem, The unit price refers to the correlation (0.35) between the active layer thickness and NDVI changes after excluding precipitation and snow water equivalent, and the grassland ecosystem service value (the unit price of tundra ecosystem service is based on 1/3 of the grassland ecosystem service value).
Zoige Wetland observation point is located at Huahu wetland (102 ° 49 ′ 09 ″ E, 33 ° 55 ′ 09 ″ N) in Zoige County, Sichuan Province, with an initial altitude of 3435 m. The underlying surface is the alpine peat wetland, with well-developed vegetation, water and peat layer. This data set is the meteorological observation data of Zoige Wetland observation point from 2017 to 2019. It is obtained by using Kipp&Zonen CNR4, Vaisala HMP155A, PTB110 and other instruments. The time resolution is half an hour, mainly including wind speed, wind direction, air temperature, relative humidity, air pressure, downward short wave radiation, downward long wave radiation.
The dataset is the remote sensing image data ofGF-1 satellite in the Qinghai-Tibet engineering corridor obtained by China High Resolution Earth Observation Center. After the fusion processing of multispectral and panchromatic bands, the image data with a spatial resolution of 2 m is obtained. In the process of obtaining ground vegetation information, the classification technology of combining object-oriented computer automatic interpretation and manual interpretation is adopted, The object-oriented classification technology is to collect adjacent pixels as objects to identify the spectral elements of interest, make full use of high-resolution panchromatic and multispectral data space, texture and spectral information to segment and classify, and output high-precision classification results or vectors. In actual operation, the image is automatically extracted by eCognition software. The main processes are image segmentation, information extraction and accuracy evaluation. After verification with the field survey, the overall extraction accuracy is more than 90%.
Firstly, the freeze thaw index is calculated by using the resampled crunep data, and then the permafrost area of circum-Arctic is predicted by the frozen number model after snow depth correction. The simulated pan Arctic permafrost area from 2000 to 2015 is 19.96 × 106 km2。 Places inconsistent with the distribution of Pan Arctic permafrost provided by the existing international snow and Ice Data Center are mainly located in island permafrost areas.
According to the inducing factors of potential thermal melting disasters (mainly thermal melting landslides) in the pan Arctic, including temperature (freezing and Thawing Environment), rainfall, snow cover, soil type, topography and landform, and underground ice content, based on the basic data provided by the big data resource database of the earth, machine learning methods (logic regression, random forest, artificial neural network, support vector machine, etc.) are adopted, and the currently interpreted thermal melting landslides in the northern hemisphere are taken as training samples, Finally, the zonation map of thermal melt disaster susceptibility (occurrence probability) in the pan Arctic was obtained. According to the sensitivity of driving factors, it is found that climate factors (temperature and rainfall) have the largest contribution to the occurrence and distribution of thermal melt disasters, followed by slope factors, and ice content and radiation also have a high contribution.
Contact SupportNorthwest Institute of Eco-Environment and Resources, CAS 0931-4967287 email@example.com
LinksNational Tibetan Plateau Data Center