Approach to calculating spatial similarity degrees of the same river basin networks on multi-scale maps

2015 ◽  
Vol 31 (7) ◽  
pp. 765-782 ◽  
Author(s):  
Haowen Yan ◽  
Yuzhong Shen ◽  
Jonathan Li
Keyword(s):  
River Basin ◽  
Multi Scale ◽  
Spatial Similarity

Quantitative Expressions of Spatial Similarity in Multi-scale Map Spaces

2017 ◽  
pp. 405-416
Author(s):  
Haowen Yan ◽  
Liming Zhang ◽  
Zhonghui Wang ◽  
Weifang Yang ◽  
Tao Liu ◽  
...  
Keyword(s):  
Multi Scale ◽  
Spatial Similarity

scholarly journals Dependence structure analysis of multisite river inflow data using vine copula-CEEMDAN based hybrid model

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10285
Author(s):  
Hafiza Mamona Nazir ◽  
Ijaz Hussain ◽  
Muhammad Faisal ◽  
Alaa Mohamd Shoukry ◽  
Mohammed Abdel Wahab Sharkawy ◽  
...  
Keyword(s):  
River Basin ◽  
Hybrid Model ◽  
Moving Average ◽  
Ensemble Empirical Mode Decomposition ◽  
Dependence Structure ◽  
Absolute Deviation ◽  
River Inflow ◽  
Multi Scale ◽  
Proposed Model ◽  
Vine Copula

Several data-driven and hybrid models are univariate and not considered the dependance structure of multivariate random variables, especially the multi-site river inflow data, which requires the joint distribution of the same river basin system. In this paper, we proposed a Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Vine copula-based approach to address this issue. The proposed hybrid model comprised on two stages: In the first stage, the CEEMDAN is used to extract the high dimensional multi-scale features. Further, the multiple models are used to predict multi-scale components and residuals. In the second stage, the residuals obtained from the first stage are used to model the joint uncertainty of multi-site river inflow data by using Canonical Vine. For the application of the proposed two-step architecture, daily river inflow data of the Indus River Basin is used. The proposed two-stage methodology is compared with only the first stage proposed model, Vector Autoregressive and copula-based Autoregressive Integrated Moving Average models. The four evaluation measures, that is, Mean Absolute Relative Error (MARE), Mean Absolute Deviation (MAD), Nash-Sutcliffe Efficiency (NSE) and Mean Square Error (MSE), are used to observe the prediction performance. The results demonstrated that the proposed model outperforms significantly with minimum MARE, MAD, NSE, and MSE for two case studies having significant joint dependance. Therefore, it is concluded that the prediction can be improved by appropriately modeling the dependance structure of the multi-site river inflow data.

scholarly journals Estimating Vegetation Primary Production in the Heihe River Basin of China with Multi-Source and Multi-Scale Data

PLoS ONE ◽  
2016 ◽  
Vol 11 (4) ◽  
pp. e0153971 ◽  
Author(s):  
Tianxiang Cui ◽  
Yujie Wang ◽  
Rui Sun ◽  
Chen Qiao ◽  
Wenjie Fan ◽  
...  
Keyword(s):  
Primary Production ◽  
River Basin ◽  
Heihe River Basin ◽  
Heihe River ◽  
Multi Scale ◽  
Scale Data ◽  
The Heihe River Basin

scholarly journals Spatiotemporal Variability of Drought and Its Multi-Scale Linkages with Climate Indices in the Huaihe River Basin, Central China and East China

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1446
Author(s):  
Guohua Fang ◽  
Xin Li ◽  
Ming Xu ◽  
Xin Wen ◽  
Xianfeng Huang
Keyword(s):  
Wavelet Transform ◽  
River Basin ◽  
Sunspot Number ◽  
Southern Oscillation ◽  
Central China ◽  
Climate Indices ◽  
Huaihe River Basin ◽  
Huaihe River ◽  
Multi Scale ◽  
The Huaihe River

With the aggravation of the ocean–atmosphere cycle anomaly, understanding the potential teleconnections between climate indices and drought/flood conditions can help us know natural hazards more comprehensively to better cope with them. This study aims at exploring the spatiotemporal patterns of drought and its multi-scale relations with typical climate indices in the Huaihe River Basin. First, the spatial patterns were identified based on the seasonal Standardized Precipitation Index (SPI)-3 during 1956–2020 by means of the Empirical Orthogonal Function (EOF). The two leading sub-regions of spring and winter droughts were determined. Then, we extracted the periodicity of spring and winter SPI-3 series and the corresponding seasonal climate indices (Arctic Oscillation (AO), Bivariate El Niño–Southern Oscillation (ENSO)Timeseries (BEST), North Atlantic Oscillation (NAO), Niño3, and Southern Oscillation Index (SOI)) and the sunspot number by using the Continuous Wavelet Transform (CWT). We further explored the teleconnections between spring drought, winter drought, and climate indices and the sunspot number by using Cross Wavelet Transform (XWT) and Wavelet Coherence (WTC) analyses. The results show that there are in-phase multi-scale relations between spring/winter PC1 and AO, BEST, and Niño3, of which the climate indices lead spring PC1 by 1.5–2 years and the climate indices lag winter PC1 by 1.5–3 years. Anti-phase relations between spring PCs and SOI and the sunspot number were observed. NAO mainly affects the interdecadal variation in spring drought, while AO and Niño3 focus on the interannual variation. In addition, Niño3 and SOI are more related to the winter drought on interdecadal scales. Moreover, there is a positive correlation between the monthly average precipitation/temperature and Niño3 with a lag of 3 months. The results are beneficial for improving the accuracy of drought prediction, considering taking NAO, AO, and Niño3 as predictors for spring drought and Niño3 and SOI for winter drought. Hence, valuable information can be provided for the management of water resources as well as early drought warnings in the basin.

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