scholarly journals Evolution of eastern Tibetan river systems is driven by the indentation of India

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Yi Chen ◽  
Baosheng Wu ◽  
Zhongyu Xiong ◽  
Jinbo Zan ◽  
Bangwen Zhang ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Large Scale ◽  
Network Models ◽  
The Tibetan Plateau ◽  
River Systems ◽  
Channel Network ◽  
Erosion Rates ◽  
Digital Elevation ◽  
Tectonically Active ◽  
Elevation Model

AbstractThe main rivers that originate from the Tibetan Plateau are important as a resource and for the sedimentary and biogeochemical exchange between mountains and oceans. However, the dominant mechanism for the evolution of eastern Tibetan river systems remains ambiguous. Here we conduct geomorphological analyses of river systems and assess catchment-average erosion rates in the eastern Tibetan Plateau using a digital elevation model and cosmogenic radionuclide data. We find that major dividing ranges have northeast oriented asymmetric geometries and that erosion rates reduce in the same direction. This coincides with the northeastward indentation of India and we suggest this indicates a primarily tectonic influence on the large-scale configuration of eastern Tibetan river systems. In contrast, low-level streams appear to be controlled by fluvial self-organization processes. We propose that this distinction between high- and low-order channel evolution highlights the importance of local optimization of optimal channel network models in tectonically active areas.

scholarly journals Evaluation of the Accuracy of SRTM3 and ASTER GDEM in the Tibetan Plateau Mountain Ranges

2020 ◽  
Vol 206 ◽  
pp. 01027
Author(s):  
Jin Yao ◽  
Yi Chao-lu ◽  
Fu Ping
Keyword(s):  
Tibetan Plateau ◽  
Digital Elevation Model ◽  
Thermal Emission ◽  
Field Measurements ◽  
The Tibetan Plateau ◽  
Mountain Areas ◽  
Aster Gdem ◽  
Digital Elevation ◽  
Vertical Accuracy ◽  
Elevation Model

Topographic data on The Tibetan Plateau (TP) terrain are fundamental for geoscientific research, but are difficult to obtain. The Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) are two commonly used GDEM data. Verifying the accuracy of the two dataset for the TP mountain areas provides a reference point for the application of both DEMs. For evaluating the elevation accuracy and topographic information, we used 8242 field measurements from Differential Global Positioning System (DGPS) points and DEM data generated from 1:100,000 topographic maps to examine the accuracy of ASTER GDEM V2 and SRTM3 V4.1 elevation results. The average RMSE for elevation differences between DGPS and ASTER GDEM across the study areas was 18.56m while the average RMSE between DGPS and SRTM3 was 10.39m. The average RMSEs of ASTER GDEM and SRTM3 in glaciated areas were 8.55m and 5.87m, respectively. The vertical accuracy of SRTM3 is better than that of ASTER GDEM. The vertical accuracy of both DEMs do not vary with altitude, but is related to aspect and slope.

scholarly journals An Automated Snow Mapper Powered by Machine Learning

2021 ◽  
Vol 13 (23) ◽  
pp. 4826
Author(s):  
Haojie Wang ◽  
Limin Zhang ◽  
Lin Wang ◽  
Jian He ◽  
Hongyu Luo
Keyword(s):  
Machine Learning ◽  
Tibetan Plateau ◽  
Large Scale ◽  
Regional Climate ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Mountainous Area ◽  
Northern Tibetan Plateau ◽  
Digital Elevation ◽  
Elevation Model

Snow preserves fresh water and impacts regional climate and the environment. Enabled by modern satellite Earth observations, fast and accurate automated snow mapping is now possible. In this study, we developed the Automated Snow Mapper Powered by Machine Learning (AutoSMILE), which is the first machine learning-based open-source system for snow mapping. It is built in a Python environment based on object-based analysis. AutoSMILE was first applied in a mountainous area of 1002 km2 in Bome County, eastern Tibetan Plateau. A multispectral image from Sentinel-2B, a digital elevation model, and machine learning algorithms such as random forest and convolutional neural network, were utilized. Taking only 5% of the study area as the training zone, AutoSMILE yielded an extraordinarily satisfactory result over the rest of the study area: the producer’s accuracy, user’s accuracy, intersection over union and overall accuracy reached 99.42%, 98.78%, 98.21% and 98.76%, respectively, at object level, corresponding to 98.84%, 98.35%, 97.23% and 98.07%, respectively, at pixel level. The model trained in Bome County was subsequently used to map snow at the Qimantag Mountain region in the northern Tibetan Plateau, and a high overall accuracy of 97.22% was achieved. AutoSMILE outperformed threshold-based methods at both sites and exhibited superior performance especially in handling complex land covers. The outstanding performance and robustness of AutoSMILE in the case studies suggest that AutoSMILE is a fast and reliable tool for large-scale high-accuracy snow mapping and monitoring.

Dynamic and Thermodynamic Relations of Distinctive Stratus Clouds on the Lee Side of the Tibetan Plateau in the Cold Season

2013 ◽  
Vol 26 (21) ◽  
pp. 8378-8391 ◽  
Author(s):  
Yi Zhang ◽  
Rucong Yu ◽  
Jian Li ◽  
Weihua Yuan ◽  
Minghua Zhang
Keyword(s):  
Tibetan Plateau ◽  
Large Scale ◽  
Eastern China ◽  
Cold Season ◽  
Water Vapor Transport ◽  
The Tibetan Plateau ◽  
Primary Role ◽  
Moist Air ◽  
Low Level ◽  
Stratus Clouds

Abstract Given the large discrepancies that exist in climate models for shortwave cloud forcing over eastern China (EC), the dynamic (vertical motion and horizontal circulation) and thermodynamic (stability) relations of stratus clouds and the associated cloud radiative forcing in the cold season are examined. Unlike the stratus clouds over the southeastern Pacific Ocean (as a representative of marine boundary stratus), where thermodynamic forcing plays a primary role, the stratus clouds over EC are affected by both dynamic and thermodynamic factors. The Tibetan Plateau (TP)-forced low-level large-scale lifting and high stability over EC favor the accumulation of abundant saturated moist air, which contributes to the formation of stratus clouds. The TP slows down the westerly overflow through a frictional effect, resulting in midlevel divergence, and forces the low-level surrounding flows, resulting in convergence. Both midlevel divergence and low-level convergence sustain a rising motion and vertical water vapor transport over EC. The surface cold air is advected from the Siberian high by the surrounding northerly flow, causing low-level cooling. The cooling effect is enhanced by the blocking of the YunGui Plateau. The southwesterly wind carrying warm, moist air from the east Bay of Bengal is uplifted by the HengDuan Mountains via topographical forcing; the midtropospheric westerly flow further advects the warm air downstream of the TP, moistening and warming the middle troposphere on the lee side of the TP. The low-level cooling and midlevel warming together increase the stability. The favorable dynamic and thermodynamic large-scale environment allows for the formation of stratus clouds over EC during the cold season.

scholarly journals The Role of Shallow Convection over the Tibetan Plateau

2017 ◽  
Vol 30 (15) ◽  
pp. 5791-5803 ◽  
Author(s):  
Yunying Li ◽  
Minghua Zhang
Keyword(s):  
Tibetan Plateau ◽  
Large Scale ◽  
Diabatic Heating ◽  
Middle Atmosphere ◽  
Atmospheric Environment ◽  
Monsoon Circulation ◽  
The Tibetan Plateau ◽  
Shallow Convection ◽  
Sensitivity Experiment ◽  
Stratiform Clouds

Cumulus (Cu) from shallow convection is one of the dominant cloud types over the Tibetan Plateau (TP) in the summer according to CloudSat– CALIPSO observations. Its thermodynamic effects on the atmospheric environment and impacts on the large-scale atmospheric circulation are studied in this paper using the Community Atmospheric Model, version 5.3 (CAM5.3). It is found that the model can reasonably simulate the unique distribution of diabatic heating and Cu over the TP. Shallow convection provides the dominant diabatic heating and drying to the lower and middle atmosphere over the TP. A sensitivity experiment indicates that without Cu over the TP, large-scale condensation and stratiform clouds would increase dramatically, which induces enhanced low-level wind and moisture convergence toward the TP, resulting in significantly enhanced monsoon circulation with remote impact on the areas far beyond the TP. Cu therefore acts as a safety valve to modulate the atmospheric environment that prevents the formation of superclusters of stratiform clouds and precipitation over the TP.

scholarly journals Automated Detection of Instability-Inducing Channel Geometry Transitions in Saint-Venant Simulation of Large-Scale River Networks

Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2236
Author(s):  
Cheng-Wei Yu ◽  
Ben R. Hodges ◽  
Frank Liu
Keyword(s):  
Large Scale ◽  
Open Channel ◽  
Search Algorithm ◽  
Shallow Water Equation ◽  
Stable Solution ◽  
River Networks ◽  
River Systems ◽  
Channel Network ◽  
Channel Geometry ◽  
Venant Equation

A new sweep-search algorithm (SSA) is developed and tested to identify the channel geometry transitions responsible for numerical convergence failure in a Saint-Venant equation (SVE) simulation of a large-scale open-channel network. Numerical instabilities are known to occur at “sharp” transitions in discrete geometry, but the identification of problem locations has been a matter of modeler’s art and a roadblock to implementing large-scale SVE simulations. The new method implements techniques from graph theory applied to a steady-state 1D shallow-water equation solver to recursively examine the numerical stability of each flowpath through the channel network. The SSA is validated with a short river reach and tested by the simulation of ten complete river systems of the Texas–Gulf Coast region by using the extreme hydrological conditions recorded during hurricane Harvey. The SSA successfully identified the problematic channel sections in all tested river systems. Subsequent modification of the problem sections allowed stable solution by an unsteady SVE numerical solver. The new SSA approach permits automated and consistent identification of problem channel geometry in large open-channel network data sets, which is necessary to effectively apply the fully dynamic Saint-Venant equations to large-scale river networks or for city-wide stormwater networks.

An analysis of an arctic channel network using a digital elevation model

Geomorphology ◽  
1999 ◽  
Vol 29 (3-4) ◽  
pp. 339-353 ◽  
Author(s):  
James P McNamara ◽  
Douglas L Kane ◽  
Larry D Hinzman
Keyword(s):  
Digital Elevation Model ◽  
Channel Network ◽  
Digital Elevation ◽  
Elevation Model

scholarly journals Fusion Analysis of Optical Satellite Images and Digital Elevation Model for Quantifying Volume in Debris Flow Disaster

2019 ◽  
Vol 11 (9) ◽  
pp. 1096 ◽  
Author(s):  
Hiroyuki Miura
Keyword(s):  
Debris Flow ◽  
Digital Elevation Model ◽  
Large Scale ◽  
Satellite Images ◽  
Erosion Depth ◽  
Digital Elevation ◽  
Elevation Model ◽  
Fusion Analysis ◽  
Optical Satellite Images ◽  
Debris Flow Disaster

Rapid identification of affected areas and volumes in a large-scale debris flow disaster is important for early-stage recovery and debris management planning. This study introduces a methodology for fusion analysis of optical satellite images and digital elevation model (DEM) for simplified quantification of volumes in a debris flow event. The LiDAR data, the pre- and post-event Sentinel-2 images and the pre-event DEM in Hiroshima, Japan affected by the debris flow disaster on July 2018 are analyzed in this study. Erosion depth by the debris flows is empirically modeled from the pre- and post-event LiDAR-derived DEMs. Erosion areas are detected from the change detection of the satellite images and the DEM-based debris flow propagation analysis by providing predefined sources. The volumes and their pattern are estimated from the detected erosion areas by multiplying the empirical erosion depth. The result of the volume estimations show good agreement with the LiDAR-derived volumes.

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