The Influence of Glacier Mass Balance on River Runoff in the Typical Alpine Basin

Yang, Bin; Du, Weibing; Li, Junli; Bao, Anming; Ge, Wen; Wang, Shuangting; Lyu, Xiaoxuan; Gao, Xin; Cheng, Xiaoqian

doi:10.3390/w15152762

Cited by 3 publications

(1 citation statement)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Glaciers are abundant in the high-cold mountains area (HCMA), which are the main source of freshwater resources in the arid and semi-arid downstream areas [2,3]. Glaciers in many of the world's HCMAs have been observed to be melting significantly, and river runoff has increased such as the Kumalak River and Tuoshikan River, typical watersheds in the Tianshan Mountains of China, where runoff has increased by 1.5 × 10 8 m 3 and 3.3 × 10 8 m 3 , respectively, over the past 50 years, leading to greater flooding [4][5][6]. Arid and semi-arid mountainous areas in northwest China are highly vulnerable to glacial snowmelt flooding under extreme climate change [7].…”

Section: Introductionmentioning

confidence: 99%

Simulation and Reconstruction of Runoff in the High-Cold Mountains Area Based on Multiple Machine Learning Models

Wang,

Sun,

Wang

et al. 2023

Water

View full text Add to dashboard Cite

Runoff from the high-cold mountains area (HCMA) is the most important water resource in the arid zone, and its accurate forecasting is key to the scientific management of water resources downstream of the basin. Constrained by the scarcity of meteorological and hydrological stations in the HCMA and the inconsistency of the observed time series, the simulation and reconstruction of mountain runoff have always been a focus of cold region hydrological research. Based on the runoff observations of the Yurungkash and Kalakash Rivers, the upstream tributaries of the Hotan River on the northern slope of the Kunlun Mountains at different time periods, and the meteorological and atmospheric circulation indices, we used feature analysis and machine learning methods to select the input elements, train, simulate, and select the preferences of the machine learning models of the runoffs of the two watersheds, and reconstruct the missing time series runoff of the Kalakash River. The results show the following. (1) Air temperature is the most important driver of runoff variability in mountainous areas upstream of the Hotan River, and had the strongest performance in terms of the Pearson correlation coefficient (ρXY) and random forest feature importance (FI) (ρXY = 0.63, FI = 0.723), followed by soil temperature (ρXY = 0.63, FI = 0.043), precipitation, hours of sunshine, wind speed, relative humidity, and atmospheric circulation were weakly correlated. A total of 12 elements were selected as the machine learning input data. (2) Comparing the results of the Yurungkash River runoff simulated by eight machine learning methods, we found that the gradient boosting and random forest methods performed best, followed by the AdaBoost and Bagging methods, with Nash–Sutcliffe efficiency coefficients (NSE) of 0.84, 0.82, 0.78, and 0.78, while the support vector regression (NSE = 0.68), ridge (NSE = 0.53), K-nearest neighbor (NSE = 0.56), and linear regression (NSE = 0.51) were simulated poorly. (3) The application of four machine learning methods, gradient boosting, random forest, AdaBoost, and bagging, to simulate the runoff of the Kalakash River for 1978–1998 was generally outstanding, with the NSE exceeding 0.75, and the results of reconstructing the runoff data for the missing period (1999–2019) could well reflect the characteristics of the intra-annual and inter-annual changes in runoff.

show abstract

Section: Introductionmentioning

confidence: 99%

Simulation and Reconstruction of Runoff in the High-Cold Mountains Area Based on Multiple Machine Learning Models

Wang,

Sun,

Wang

et al. 2023

Water

View full text Add to dashboard Cite

show abstract

Remote Sensing Evaluation and Monitoring of Spatial and Temporal Changes in Ecological Environmental Quality in Coal Mining-Intensive Cities

Huo,

Cheng,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

In coal-dependent urban economies, the dichotomy between resource exploitation and ecological conservation presents a pronounced challenge. Traditional remote sensing ecological assessments often overlook the interplay between mining activities and urban environmental dynamics. To address this gap, researchers developed an innovative Resource-Based City Ecological Index (RCEI), anchored in a Pressure–State–Response (PSR) framework and synthesized from six discrete ecological indicators. Utilizing geodetic remote sensing data, the RCEI facilitated a comprehensive spatiotemporal analysis of Jincheng City’s ecological quality from 1990 to 2022. The findings corroborated the RCEI’s efficacy in providing a nuanced portrayal of the ecological state within mining regions. (1) Jincheng City’s ecological quality predominantly sustained a mudhopper-tier status, exhibiting an overarching trend of amelioration throughout the study period. (2) Disparities in ecological landscape quality were pronounced at the county level, with Moran’s Index exceeding 0.9, signifying a clustered ecological quality pattern. High–high (H–H) zones were prevalent in areas of elevated altitude and dense vegetation, whereas low–low (L–L) zones were prevalent in urban and mining sectors. (3) Further, a buffer zone analysis of two coal mines, differing in their mining chronology, geographical positioning, and operational status, elucidated the ecological impact exerted over a 32-year trajectory. These insights furnish a robust scientific and technical foundation for resource-centric cities to fortify ecological safeguarding and to advance sustainable development stratagems.

show abstract

Reconstruction of Coal Mining Subsidence Field by Fusion of SAR and UAV LiDAR Deformation Data

Yang,

Du,

Zou

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

The geological environment damage caused by coal mining subsidence has become an important factor affecting the sustainable development of mining areas. Reconstruction of the Coal Mining Subsidence Field (CMSF) is the key to preventing geological disasters, and the needs of CMSF reconstruction cannot be met by solely relying on a single remote sensing technology. The combination of Unmanned Aerial Vehicle (UAV) and Synthetic Aperture Radar (SAR) has complementary advantages; however, the data fusion strategy by refining the SAR deformation field through UAV still needs to be updated constantly. This paper proposed a Prior Weighting (PW) method based on Satellite Aerial (SA) heterogeneous remote sensing. The method can be used to fuse SAR and UAV Light Detection and Ranging (LiDAR) data for ground subsidence parameter inversion. Firstly, the subsidence boundary of Differential Interferometric SAR (DInSAR) combined with the large gradient subsidence of Pixel Offset Tracking (POT) was developed to initialize the SAR preliminary CMSF. Secondly, the SAR preliminary CMSF was refined by UAV LiDAR data; the weights of SAR and UAV LiDAR data are 0.4 and 0.6 iteratively. After the data fusion, the subsidence field was reconstructed. The results showed that the overall CMSF accuracy improved from ±144 mm to ±51 mm. The relative errors of the surface subsidence factor and main influence angle tangent calculated by the physical model and in situ measured data are 1.3% and 1.7%. It shows that the proposed SAR/UAV fusion method has significant advantages in the reconstruction of CMSF, and the PW method contributes to the prevention and control of mining subsidence.

show abstract

The Influence of Glacier Mass Balance on River Runoff in the Typical Alpine Basin

Cited by 3 publications

References 72 publications

Simulation and Reconstruction of Runoff in the High-Cold Mountains Area Based on Multiple Machine Learning Models

Simulation and Reconstruction of Runoff in the High-Cold Mountains Area Based on Multiple Machine Learning Models

Remote Sensing Evaluation and Monitoring of Spatial and Temporal Changes in Ecological Environmental Quality in Coal Mining-Intensive Cities

Reconstruction of Coal Mining Subsidence Field by Fusion of SAR and UAV LiDAR Deformation Data

Contact Info

Product

Resources

About