A preliminary investigation on the climate-discharge relationship in the upper region of the Yarlung Zangbo River basin

2021 ◽  
pp. 127066
Author(s):  
Suning Liu ◽  
Yingying Yao ◽  
Xingxing Kuang ◽  
Chunmiao Zheng
Keyword(s):  
River Basin ◽  
Preliminary Investigation ◽  
Yarlung Zangbo River

scholarly journals Appropriateness of Potential Evapotranspiration Models for Climate Change Impact Analysis in Yarlung Zangbo River Basin, China

Atmosphere ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 453 ◽  
Author(s):  
Pan ◽  
Xu ◽  
Xuan ◽  
Gu ◽  
Bai
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Climate Change Impact ◽  
Impact Analysis ◽  
Potential Evapotranspiration ◽  
Global Climate Models ◽  
Historical Period ◽  
Yarlung Zangbo River ◽  
The Future ◽  
Change Impact

Evapotranspiration (ET) is an important element in the water and energy cycle. Potential evapotranspiration (PET) is an important measurement of ET. Its accuracy has significant influence on agricultural water management, irrigation planning, and hydrological modelling. However, whether current PET models are applicable under climate change or not, is still a question. In this study, five frequently used PET models were chosen, including one combination model (the FAO Penman-Monteith model, FAO-PM), two temperature-based models (the Blaney-Criddle and the Hargreaves models) and two radiation-based models (the Makkink and the Priestley-Taylor models), to estimate their appropriateness in the historical and future periods under climate change impact on the Yarlung Zangbo river basin, China. Bias correction methods were not only applied to the temperature output of Global Climate Models (GCMs), but also for radiation, humidity, and wind speed. It was demonstrated that the results from the Blaney-Criddle and Makkink models provided better agreement with the PET obtained by the FAO-PM model in the historical period. In the future period, monthly PET estimated by all five models show positive trends. The changes of PET under RCP8.5 are much higher than under RCP2.6. The radiation-based models show better appropriateness than the temperature-based models in the future, as the root mean square error (RMSE) value of the former models is almost half of the latter models. The radiation-based models are recommended for use to estimate PET under climate change in the Yarlung Zangbo river basin.

Responses of hydrological processes to climate change in the Yarlung Zangbo River basin

2019 ◽  
Vol 64 (16) ◽  
pp. 2057-2067 ◽  
Author(s):  
Xiaowan Liu ◽  
Zongxue Xu ◽  
Wenfeng Liu ◽  
Liu Liu
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Hydrological Processes ◽  
Yarlung Zangbo River

Temporal and spatial variations of δ18O in precipitation of the Yarlung Zangbo River Basin

2007 ◽  
Vol 17 (3) ◽  
pp. 317-326 ◽  
Author(s):  
Zhongfang Liu ◽  
Lide Tian ◽  
Tandong Yao ◽  
Tongliang Gong ◽  
Changliang Yin ◽  
...  
Keyword(s):  
River Basin ◽  
Spatial Variations ◽  
Temporal And Spatial Variations ◽  
Yarlung Zangbo River ◽  
Temporal And Spatial

scholarly journals Spatial Downscaling of TRMM Precipitation Data Using an Optimal Subset Regression Model with NDVI and Terrain Factors in the Yarlung Zangbo River Basin, China

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Jinping Liu ◽  
Wanchang Zhang ◽  
Ning Nie
Keyword(s):  
River Basin ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Rain Gauge ◽  
Precipitation Data ◽  
Rain Gauge Data ◽  
Optimal Subset ◽  
Yarlung Zangbo River ◽  
Gauge Data ◽  
Subset Regression

High accuracy, high spatial resolution precipitation data is important for understanding basin-scale hydrology and the spatiotemporal distributions of regional precipitation. The objective of this study was to develop a reliable statistical downscaling algorithm to produce high quality, high spatial resolution precipitation products from Tropical Rainfall Monitoring Mission (TRMM) 3B43 data over the Yarlung Zangbo River Basin using an optimal subset regression (OSR) model combined with multiple topographical factors, the Normalized Difference Vegetation Index (NDVI), and observational data from rain gauge stations. After downscaling, the bias between TRMM 3B43 and rain gauge data decreased considerably from 0.397 to 0.109, the root-mean-square error decreased from 235.16 to 124.60 mm, and the r2 increased from 0.54 to 0.61, indicating significant improvement in the spatial resolution and accuracy of the TRMM 3B43 data. Moreover, the spatial patterns of both precipitation rates of change and their corresponding p value statistics were consistent between the downscaled results and the original TRMM 3B43 during the 2001–2014 period, which verifies that the downscaling method performed well in the Yarlung Zangbo River Basin. Its high performance in downscaling precipitation was also proven by comparing with other models. All of these findings indicate that the proposed approach greatly improved the quality and spatial resolution of TRMM 3B43 rainfall products in the Yarlung Zangbo River Basin, for which rain gauge data is limited. The potential of the post-real-time Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) downscaled precipitation product was also demonstrated in this study.

scholarly journals Modelling the Vegetation Response to Climate Changes in the Yarlung Zangbo River Basin Using Random Forest

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1433 ◽  
Author(s):  
Kaige Chi ◽  
Bo Pang ◽  
Lizhuang Cui ◽  
Dingzhi Peng ◽  
Zhongfan Zhu ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
River Basin ◽  
Partial Correlation ◽  
Climatic Factors ◽  
Vegetation Coverage ◽  
Support Vector ◽  
Partial Correlation Analysis ◽  
Proposed Model ◽  
Yarlung Zangbo River

Vegetation coverage variation may influence watershed water balance and water resource availability. Yarlung Zangbo River, the longest river on the Tibetan Plateau, has high spatial heterogeneity in vegetation coverage and is the main freshwater resource of local residents and downstream countries. In this study, we proposed a model based on random forest (RF) to predict the Normalized Difference Vegetation Index (NDVI) of the Yarlung Zangbo River Basin and explore its relationship with climatic factors. High-resolution datasets of NDVI and monthly meteorological observation data from 2000 to 2015 were used to calibrate and validate the proposed model. The proposed model was then compared with artificial neural network and support vector machine models, and principal component analysis and partial correlation analysis were also used for predictor selection of artificial neural network and support vector machine models for comparative study. The results show that RF had the highest model efficiency among the compared models. The Nash–Sutcliffe coefficients of the proposed model in the calibration period and verification period were all higher than 0.8 for the five subzones; this indicated that the proposed model can successfully simulate the relationship between the NDVI and climatic factors. By using built-in variable importance evaluation, RF chose appropriate predictor combinations without principle component analysis or partial correlation analysis. Our research is valuable because it can be integrated into water resource management and elucidates ecological processes in Yarlung Zangbo River Basin.

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